Atlassian Intelligence designed for transparency
Our no b.s. commitment to open communication, accountability, and helping teams to use AI responsibly.
Atlassian Intelligence is designed to fast-track collaboration and empower teams to accelerate their work. And just like getting to know how to best work with your team, understanding how Atlassian Intelligence works will help teams more effectively use it. On this page, we’ll explain how our AI-powered products and features work, including what they can and cannot do, and how they contribute to the way that you experience our products. We believe that equipping you with the information on this page will help you make the most out of our products — and your teamwork. To learn more about our commitment to building technology responsibly visit our Responsible Technology Principles.
Alert grouping
Alert grouping by Atlassian Intelligence is powered by large language models developed by OpenAI and other machine learning models. These models include the OpenAI models described here. Atlassian Intelligence uses these machine learning models to analyze and generate alert groups and give related suggestions (past alert groups and past alert responders) within our products based on the similarity of the alert content or the tags used. Atlassian Intelligence then uses large language models to analyze and generate natural language descriptions and content for these groups within our products. These large language models generate responses based on your inputs and are probabilistic. This means that their responses are generated by predicting the most probable next word or text based on the data they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Alert grouping uses Atlassian Intelligence to identify and group similar alerts together. It also helps you by identifying and recommending past similar alert groups and past alert responders (or teams of responders), based on the semantic similarity of the alert content or tags used. When you want to escalate the alert group to an incident, alert grouping will also pre-populate all contextual information for you to review as part of the incident creation process. We believe that alert grouping works best in scenarios where:
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It’s important to remember that because of the way that the models used to power alert grouping work, these models can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on or include content that sounds reasonable but is false or incomplete. In the case of the alert groups that you see, they might not precisely reflect the semantic similarity of their tags. We’ve found that alert grouping is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about ensuring that you and your team follow consistent practices in using alert tags. |
We understand you may have questions about how alert grouping uses your data. This section supplements the information available on our FAQ page. We process:
We process your alert data to train a version of the machine learning model to recognize patterns specific to your alerts. This version is used to serve only your experience:
When it comes to your data, alert grouping applies the following measures:
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Atlassian Intelligence answers in Jira Service Management
Atlassian Intelligence answers is powered by large language models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
The Atlassian Intelligence answers feature connects to the virtual service agent in Jira Service Management. It uses generative artificial intelligence to search across your linked knowledge base spaces and answer your customer questions. We believe that Atlassian Intelligence answers works best in scenarios where:
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It’s important to remember that because of the way that the models used to power Atlassian Intelligence answers work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that Atlassian Intelligence answers is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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We understand you may have questions about how Atlassian Intelligence answers in Jira Service Management uses your data. This section supplements the information available on our FAQ page. We process:
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Automation using Atlassian Intelligence
Automation using Atlassian Intelligence is powered by GPT models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze natural language input and generate an automation rule for you within Jira and Confluence. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated by predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Creating automation rules is at the core of the everyday automation experience, and we want to make this even easier for you by adding Atlassian Intelligence to the automation rule builder in Jira and Confluence. Now, you can easily create automation rules by simply typing in and describing what you wish to automate, and let Atlassian Intelligence handle all the heavy lifting of creating the rule for you. Find out more about Automation using Atlassian Intelligence for Jira and for Confluence. We believe that Automation using Atlassian Intelligence for Jira and Confluence works best in scenarios when you are not sure how to get started or want to accelerate the rule creation process. Not sure how best to create an automation rule?Automation rules are created by a combination of different types of components: triggers, actions, conditions, and branches. Think of components as the building blocks of a rule. To successfully create a rule with Atlassian Intelligence, your rule must at least contain both a trigger and an action. For example: In Jira: Every Monday, find all the tasks with a due date in the next 7 days, and send the assignee a reminder email. When a ticket moves to Testing, assign the ticket to John Smith. In Confluence:
In addition, for a rule to be successfully created, all its components must be supported by automation using Atlassian Intelligence. This means that any triggers, actions, conditions, or branches in your rule must be compatible with automation in Jira and/or Confluence. |
It’s important to remember that because of the way that the models used to power automation using Atlassian Intelligence work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that automation using Atlassian Intelligence is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. Automation using Atlassian Intelligence will only work with the existing set of available automation components in Jira and Confluence. You might also want to think about being as specific as possible in what you ask Atlassian Intelligence to do, as described above. |
We understand you may have questions about how Automation using Atlassian Intelligence uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, using Atlassian Intelligence for Confluence automation applies the following measures:
OpenAI is a subprocessor on our List of Subprocessors. They do not use your inputs and outputs for any purpose besides processing your request. This feature follows the permissions in your instance. For example, if you do not have access to a specific project or page, you will not be suggested content from those assets in the response you receive. If you do not want your content to be available in responses to other users in your instance, work with your org admin to ensure your permissions are set appropriately. |
Chart Insights
Chart insights is powered by large language models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated by predicting the most probable next word or text based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Chart insights uses Atlassian Intelligence to help speed up your understanding of data in any chart in Atlassian Analytics. It does so by using the dashboard title, chart title, and chart data (including column headers and row values) to generate a natural language summary of that chart and its data. It will also aim to identify any trends or anomalies to provide you with certain insights into that chart. We believe that chart insights work best in scenarios where:
Bar charts, line charts, and bar-line charts work best with this feature since they typically have trends, dates, and many rows of data. |
It’s important to remember that because of the way that the models used to power chart insights work, these models can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that chart insights is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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We understand you may have questions about how chart insights uses your data. This section supplements the information available on this page We process:
When it comes to your data, chart insights applies the following measures.
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Confluence quick summary
Summarize pages and blogs using Atlassian Intelligence is powered by LLM models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Save time and get the details you need to do your work faster by generating a quick summary of a Confluence page or blog with Atlassian Intelligence. Find out more about using Atlassian Intelligence in Confluence. We believe that summarizing pages and blogs using Atlassian Intelligence works best in scenarios when:
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It’s important to remember that because of the way that the models used to power summarizing pages and blogs using Atlassian Intelligence work, they can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on or include content that sounds reasonable but is false or incomplete. While we continue to build better support for macros, tables, and expand in summaries, we’ve found that summarizing pages and blogs using Atlassian Intelligence is less useful in scenarios where:
We encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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We understand you may have questions about how using Atlassian Intelligence for Confluence automation uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, summarizing pages and blogs using Atlassian Intelligence applies the following measures:
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Define terms using Atlassian Intelligence
Defining terms using Atlassian Intelligence in Confluence and Jira is powered by large language models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language answers within Confluence. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. |
One of the most challenging things about consuming content in Confluence and Jira can be getting the context you need to understand what you’re reading. Abbreviations, acronyms, unfamiliar terms, and team or project-specific names can lead to a lengthy search for the information you need. Defining terms using Atlassian Intelligence will provide the definition of company-specific terms (such as acronyms, project, system, or team names) on a page in Confluence or in an issue description in Jira. This gives users the information they need, when they need it - all whilst helping teams work better together. Atlassian Intelligence can save you time by defining these things for you, without navigating away from what you’re reading. If you encounter a definition that you feel is inaccurate, you can edit or add a new definition, then set the visibility to be for that page or issue, the whole space or project, or access your entire organization. We believe that defining terms using Atlassian Intelligence in Confluence works best in scenarios when:
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It’s important to remember that because of the way that the models used to define terms using Atlassian Intelligence in Confluence work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that defining terms using Atlassian Intelligence in Confluence is less useful in scenarios where:
In addition, in Jira, we've also found that because defining terms using Atlassian Intelligence relies on search in Confluence, the feature will only work in Jira if you have permission to view a Confluence instance on the same site as your Jira instance. It's also possible that you might find that defining terms using Atlassian Intelligence doesn't perform as expected in Confluence spaces or Jira instances that have content written in multiple languages. |
We understand you may have questions about how defining terms using Atlassian Intelligence uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, defining terms using Atlassian Intelligence applies the following measures:
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Generate pull request descriptions with Atlassian Intelligence
Generating pull request descriptions with Atlassian Intelligence is powered by large language models (LLM) developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language and code within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated by predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Atlassian Intelligence can help you generate, transform, and summarize content while you're writing pull request descriptions or comments in the Bitbucket Cloud code review experience. This includes:
We believe that generating Bitbucket Cloud pull request descriptions with Atlassian Intelligence works best in scenarios where:
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It’s important to remember that because of the way that the models used to power this feature work, these models can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on or include content that sounds reasonable but is false or incomplete. We’ve found that generating Bitbucket Cloud pull request descriptions with Atlassian Intelligence is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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We understand you may have questions about how defining terms using Atlassian Intelligence in Confluence uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, generating pull request descriptions with Atlassian Intelligence applies the following measures:
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Generate SQL queries in Atlassian Analytics
Generating SQL queries using Atlassian Intelligence in Atlassian Analytics is powered by large language models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and understand natural language, then translates it to structured query language (SQL) within Atlassian Analytics. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Ask Atlassian Intelligence a question in natural language and have it translated into SQL, rather than writing your own SQL queries from scratch. After you ask a question, Atlassian Intelligence uses the Atlassian Data Lake schema of your selected data source to generate an SQL query that can be used to build charts on your Atlassian Analytics dashboards, and can also help you learn about the schema in the Data Lake. We believe that generating SQL queries using Atlassian Intelligence works best in scenarios where:
Not sure what questions to ask?Here are some suggestions:
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It’s important to remember that because of the way that the models used to generate SQL queries using Atlassian Intelligence work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that generating SQL queries using Atlassian Intelligence is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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We understand you may have questions about how generating SQL queries using Atlassian Intelligence uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, generating SQL queries using Atlassian Intelligence applies the following measures.
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Generative AI in the editor
Atlassian Intelligence in editing experiences is powered by large language models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Atlassian Intelligence helps drive effective communication across all teams in an organization to improve efficiency, decision-making, and processes. We believe that using Atlassian Intelligence in editing experiences works best in scenarios like:
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It’s important to remember that because of the way that the models used to power Atlassian Intelligence in editing experiences work, they can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content they’re based on or include content that sounds reasonable but is false or incomplete. We’ve found that using Atlassian Intelligence in editing experiences is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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We understand you may have questions about how Atlassian Intelligence in editing experiences uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, Atlassian Intelligence in editing experiences applies the following measures:
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Search answers in Confluence
Search answers in Confluence using Atlassian Intelligence is powered by LLM models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Knowledge bases are growing too fast for users to keep up. Searching answers in Confluence using Atlassian Intelligence provides a faster path to key information that customers need to move their work forward. This feature helps you easily find the information you need. It understands the types of questions you would ask a teammate, and answers them instantly. Find out more about using Atlassian Intelligence to search for answers in Confluence. We believe that searching answers in Confluence using Atlassian Intelligence works best when your Confluence site is full of detailed, complete, and up-to-date content. This feature does not generate new content, but searches Confluence pages and blogs (while respecting restrictions) to find an answer to your question. Atlassian Intelligence generates answers solely based on what’s in your Confluence, and what you, specifically, have access to. Not sure what questions to ask?Here are some suggestions
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We understand you may have questions about how searching answers in Confluence using Atlassian Intelligence uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, searching answers in Confluence using Atlassian Intelligence applies the following measures:
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Search issues in Jira
Search issues using Atlassian Intelligence in Jira is powered by large language models developed by Open AI. The models include the OpenAI models described here, fine-tuned by Atlassian using generated synthetic data. Atlassian Intelligence uses these models to analyze and understand natural language, then translates it to Jira Query Language (JQL) code within our products. These models generate responses based on your inputs and are probabilistic in nature. This means their responses are generated by predicting the most probable next word or text based on the data they have been trained on. Read more about the capabilities of OpenAI models, and OpenAI fine-tuning. You can also read more about this approach in OpenAI’s research papers. |
You can now ask Atlassian Intelligence what you want in everyday language instead of coming up with complex queries. By searching issues using Atlassian Intelligence, your prompt is translated into a JQL query which quickly assists you in your search for specific issues. We believe searching issues using Atlassian Intelligence works best in scenarios where:
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It's important to remember that because of the way that the models used to search issues using Atlassian Intelligence work, these models can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses you receive might not accurately reflect the content they are based on or include content that sounds reasonable but is false or incomplete. We've found that searching issues using Atlassian Intelligence is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about being as specific as possible in what you ask Atlassian Intelligence to do. Make sure to include the exact fields and values you're looking for. |
We understand you may have questions about how searching issues using Atlassian Intelligence uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, searching issues using Atlassian Intelligence applies the following measures:
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Suggest request types in Jira Service Management
Suggest request types using Atlassian Intelligence is powered by large language models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze natural language input and generate recommendations for request type names and descriptions for you within Jira Service Management. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Spend less time figuring out what kind of request types you need to create for your project, and instead get suggestions from Atlassian Intelligence. Simply describe your work and what your team typically manages, to see what types of requests you could create. Select one of the suggestions generated by Atlassian Intelligence to create a request type. Find out more about how to use Atlassian Intelligence to suggest request types. We believe that using Atlassian Intelligence to suggest request types works best in scenarios where:
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It’s important to remember that because of the way that the models used to suggest request types using Atlassian Intelligence work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that using Atlassian Intelligence to suggest request types is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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We understand you may have questions about how using Atlassian Intelligence to suggest request types uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, using Atlassian Intelligence to suggest request types applies the following measures.
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Summarize issue details in Jira Service Management
Summarize issue details using Atlassian Intelligence is powered by large language models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Instead of reading through long descriptions and numerous comments on a Jira Service Management issue, you can use Atlassian Intelligence to quickly summarize this information for you. This helps agents quickly understand the context of the issue and any progress made, enabling them to take swift action and provide timely assistance. We believe that summarizing issue details using Atlassian Intelligence works best for:
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It’s important to remember that because of the way that the models used to power summarizing issue details using Atlassian Intelligence work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that summarizing issue details using Atlassian Intelligence is less useful in scenarios when:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. |
We understand you may have questions about how summarizing issue details using Atlassian Intelligence uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, summarizing issue details using Atlassian Intelligence applies the following measures:
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Write custom formulas using Atlassian Intelligence
Writing custom formulas using Atlassian Intelligence is powered by large language models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze natural language and then translates it to SQLite within Atlassian Analytics. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated by predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Ask or describe to Atlassian Intelligence how you want to transform the data in your result table and it will translate it into a SQLite expression that’s used for custom formulas, rather than writing your own SQLite expressions from scratch. After you ask a question, Atlassian Intelligence uses the data in the result table of the previous Visual SQL step to generate an SQLite expression that applies calculations or operations to that data for your chart. This can also help you learn about SQLite functions and their syntax. Writing custom formulas using Atlassian Intelligence works best in scenarios where:
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When using custom formulas, remember that the models used in Atlassian Intelligence can sometimes behave inaccurately, incompletely or unreliably. For example, the responses you receive might not accurately reflect the content they're based on, or include content that sounds reasonable but is false or incomplete. We’ve found that writing custom formulas using Atlassian Intelligence is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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We understand you may have questions about how your data is used when writing custom formulas using Atlassain Intelligence. This section supplements the information available on our Trust Center. We process:
When it comes to your data, writing custom formulas using Atlassian Intelligence applies the following measures. Your prompts (inputs) and responses (outputs):
OpenAI is a subprocessor on our List of Subprocessors. They do not use your inputs and outputs for any purpose besides processing your request. |
Read more about Atlassian Intelligence
Discover more about using Atlassian Intelligence
Find out how to use Atlassian Intelligence to search for answers in Confluence
Atlassian Intelligence and Rovo are designed for transparency
Our no b.s. commitment to open communication, accountability, and helping teams to use AI responsibly.
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Select a Rovo feature below to get a transparent look at use cases and data use.
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Select an Atlassian Intelligence feature below to get a transparent look at use cases and data use.
Atlassian Intelligence answers is powered by large language models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
The Atlassian Intelligence answers feature connects to the virtual service agent in Jira Service Management. It uses generative artificial intelligence to search across your linked knowledge base spaces and answer your customer questions. We believe that Atlassian Intelligence answers works best in scenarios where:
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It’s important to remember that because of the way that the models used to power Atlassian Intelligence answers work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that Atlassian Intelligence answers is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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We understand you may have questions about how Atlassian Intelligence answers in Jira Service Management uses your data. This section supplements the information available on our FAQ page. We process:
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Automation using Atlassian Intelligence is powered by GPT models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze natural language input and generate an automation rule for you within Jira and Confluence. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated by predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Creating automation rules is at the core of the everyday automation experience, and we want to make this even easier for you by adding Atlassian Intelligence to the automation rule builder in Jira and Confluence. Now, you can easily create automation rules by simply typing in and describing what you wish to automate, and let Atlassian Intelligence handle all the heavy lifting of creating the rule for you. Find out more about Automation using Atlassian Intelligence for Jira and for Confluence. We believe that Automation using Atlassian Intelligence for Jira and Confluence works best in scenarios when you are not sure how to get started or want to accelerate the rule creation process. Not sure how best to create an automation rule?Automation rules are created by a combination of different types of components: triggers, actions, conditions, and branches. Think of components as the building blocks of a rule. To successfully create a rule with Atlassian Intelligence, your rule must at least contain both a trigger and an action. For example: In Jira: Every Monday, find all the tasks with a due date in the next 7 days, and send the assignee a reminder email. When a ticket moves to Testing, assign the ticket to John Smith. In Confluence:
In addition, for a rule to be successfully created, all its components must be supported by Automation using Atlassian Intelligence. This means that any triggers, actions, conditions, or branches in your rule must be compatible with Automation in Jira and/or Confluence. |
It’s important to remember that because of the way that the models used to power Automation using Atlassian Intelligence work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that Automation using Atlassian Intelligence is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. Automation using Atlassian Intelligence will only work with the existing set of available automation components in Jira and Confluence. You might also want to think about being as specific as possible in what you ask Atlassian Intelligence to do, as described above. |
We understand you may have questions about how Automation using Atlassian Intelligence uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, using Atlassian Intelligence for Confluence automation applies the following measures:
OpenAI is a subprocessor on our List of Subprocessors. They do not use your inputs and outputs for any purpose besides processing your request. This feature follows the permissions in your instance. For example, if you do not have access to a specific project or page, you will not be suggested content from those assets in the response you receive. If you do not want your content to be available in responses to other users in your instance, work with your org admin to ensure your permissions are set appropriately. |
Alert grouping by Atlassian Intelligence is powered by large language models developed by OpenAI and other machine learning models. These models include the OpenAI models described here. Atlassian Intelligence uses these machine learning models to analyze and generate alert groups and give related suggestions (past alert groups and past alert responders) within our products based on the similarity of the alert content or the tags used. Atlassian Intelligence then uses large language models to analyze and generate natural language descriptions and content for these groups within our products. These large language models generate responses based on your inputs and are probabilistic. This means that their responses are generated by predicting the most probable next word or text based on the data they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Alert grouping uses Atlassian Intelligence to identify and group similar alerts together. It also helps you by identifying and recommending past similar alert groups and past alert responders (or teams of responders), based on the semantic similarity of the alert content or tags used. When you want to escalate the alert group to an incident, alert grouping will also pre-populate all contextual information for you to review as part of the incident creation process. We believe that alert grouping works best in scenarios where:
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It’s important to remember that because of the way that the models used to power alert grouping work, these models can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on or include content that sounds reasonable but is false or incomplete. In the case of the alert groups that you see, they might not precisely reflect the semantic similarity of their tags. We’ve found that alert grouping is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about ensuring that you and your team follow consistent practices in using alert tags. |
We understand you may have questions about how alert grouping uses your data. This section supplements the information available on our FAQ page. We process:
We process your alert data to train a version of the machine learning model to recognize patterns specific to your alerts. This version is used to serve only your experience:
When it comes to your data, alert grouping applies the following measures:
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Summarize pages and blogs using Atlassian Intelligence is powered by LLM models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Save time and get the details you need to do your work faster by generating a quick summary of a Confluence page or blog with Atlassian Intelligence. Find out more about using Atlassian Intelligence in Confluence. We believe that summarizing pages and blogs using Atlassian Intelligence works best in scenarios when:
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It’s important to remember that because of the way that the models used to power summarizing pages and blogs using Atlassian Intelligence work, they can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on or include content that sounds reasonable but is false or incomplete. While we continue to build better support for macros, tables, and expand in summaries, we’ve found that summarizing pages and blogs using Atlassian Intelligence is less useful in scenarios where:
We encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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We understand you may have questions about how using Atlassian Intelligence for Confluence automation uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, summarizing pages and blogs using Atlassian Intelligence applies the following measures:
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Defining terms using Atlassian Intelligence in Confluence and Jira is powered by large language models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language answers within Confluence. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. |
One of the most challenging things about consuming content in Confluence and Jira can be getting the context you need to understand what you’re reading. Abbreviations, acronyms, unfamiliar terms, and team or project-specific names can lead to a lengthy search for the information you need. Defining terms using Atlassian Intelligence will provide the definition of company-specific terms (such as acronyms, project, system, or team names) on a page in Confluence or in an issue description in Jira. This gives users the information they need, when they need it - all whilst helping teams work better together. Atlassian Intelligence can save you time by defining these things for you, without navigating away from what you’re reading. If you encounter a definition that you feel is inaccurate, you can edit or add a new definition, then set the visibility to be for that page or issue, the whole space or project, or access your entire organization. We believe that defining terms using Atlassian Intelligence in Confluence works best in scenarios when:
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It’s important to remember that because of the way that the models used to define terms using Atlassian Intelligence in Confluence work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that defining terms using Atlassian Intelligence in Confluence is less useful in scenarios where:
In addition, in Jira, we've also found that because defining terms using Atlassian Intelligence relies on search in Confluence, the feature will only work in Jira if you have permission to view a Confluence instance on the same site as your Jira instance. It's also possible that you might find that defining terms using Atlassian Intelligence doesn't perform as expected in Confluence spaces or Jira instances that have content written in multiple languages. |
We understand you may have questions about how defining terms using Atlassian Intelligence uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, defining terms using Atlassian Intelligence applies the following measures:
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Atlassian Intelligence in editing experiences is powered by large language models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Atlassian Intelligence helps drive effective communication across all teams in an organization to improve efficiency, decision-making, and processes. We believe that using Atlassian Intelligence in editing experiences works best in scenarios like:
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It’s important to remember that because of the way that the models used to power Atlassian Intelligence in editing experiences work, they can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content they’re based on or include content that sounds reasonable but is false or incomplete. We’ve found that using Atlassian Intelligence in editing experiences is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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We understand you may have questions about how Atlassian Intelligence in editing experiences uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, Atlassian Intelligence in editing experiences applies the following measures:
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Summarize issue details in Jira Service Management
Summarize issue details using Atlassian Intelligence is powered by large language models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Instead of reading through long descriptions and numerous comments on a Jira Service Management issue, you can use Atlassian Intelligence to quickly summarize this information for you. This helps agents quickly understand the context of the issue and any progress made, enabling them to take swift action and provide timely assistance. We believe that summarizing issue details using Atlassian Intelligence works best for:
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It’s important to remember that because of the way that the models used to power summarizing issue details using Atlassian Intelligence work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that summarizing issue details using Atlassian Intelligence is less useful in scenarios when:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. |
We understand you may have questions about how summarizing issue details using Atlassian Intelligence uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, summarizing issue details using Atlassian Intelligence applies the following measures:
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Summarize Smart Links with AI is powered by large language models developed by Open AI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
After you hover over a Smart Link from Jira, Confluence, and Google Docs, Atlassian Intelligence can help you summarize the content, which allows you to determine the importance and value of the link and decide your next action. This reduces the need to leave the current page and switch contexts. We believe that Summarize Smart Links with AI works best in scenarios where:
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It’s important to remember that because of the way that the models used to power Summarize Smart Links with AI work, these models can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the summaries you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that Summarize Smart Links with AI is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. |
We understand you may have questions about how summarizing issue details using Atlassian Intelligence uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, Summarize Smart Links with AI applies the following measures. Your summaries:
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Writing custom formulas using Atlassian Intelligence is powered by large language models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze natural language and then translates it to SQLite within Atlassian Analytics. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated by predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Ask or describe to Atlassian Intelligence how you want to transform the data in your result table and it will translate it into a SQLite expression that’s used for custom formulas, rather than writing your own SQLite expressions from scratch. After you ask a question, Atlassian Intelligence uses the data in the result table of the previous Visual SQL step to generate an SQLite expression that applies calculations or operations to that data for your chart. This can also help you learn about SQLite functions and their syntax. Writing custom formulas using Atlassian Intelligence works best in scenarios where:
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When using custom formulas, remember that the models used in Atlassian Intelligence can sometimes behave inaccurately, incompletely or unreliably. For example, the responses you receive might not accurately reflect the content they're based on, or include content that sounds reasonable but is false or incomplete. We’ve found that writing custom formulas using Atlassian Intelligence is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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We understand you may have questions about how using Atlassian Intelligence for Confluence automation uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, summarizing pages and blogs using Atlassian Intelligence applies the following measures:
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Use AI to drive action
Select an Atlassian Intelligence feature below to get a transparent look at use cases and data use.
Summarize pages and blogs using Atlassian Intelligence is powered by LLM models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
When escalating one or more alerts or alert groups to an incident in Jira Service Management, create incident with AI uses Atlassian Intelligence to quickly pre-populate all contextual information from for you to review as part of the incident creation process. This allows users to quickly understand the context of the incident created from those alerts or alert groups, and review and confirm pre-populated information including the title, description and priority of the alert when escalating it to an incident. We believe that create incident with AI works best in scenarios where:
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It’s important to remember that because of the way that the models used to power create incident with AI work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that create incident with AI is less useful in scenarios when:
For these reasons, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. To get the most useful results we suggest being as specific as possible in what you ask Atlassian Intelligence to do. You might also want to think about being as specific as possible in what you ask Atlassian Intelligence to do. |
We understand you may have questions about how create incident with AI uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, create incident with AI applies the following measures:
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Generating pull request descriptions with Atlassian Intelligence is powered by large language models (LLM) developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language and code within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated by predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Atlassian Intelligence can help you generate, transform, and summarize content while you're writing pull request descriptions or comments in the Bitbucket Cloud code review experience. This includes:
We believe that generating Bitbucket Cloud pull request descriptions with Atlassian Intelligence works best in scenarios where:
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It’s important to remember that because of the way that the models used to power this feature work, these models can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on or include content that sounds reasonable but is false or incomplete. We’ve found that generating Bitbucket Cloud pull request descriptions with Atlassian Intelligence is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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We understand you may have questions about how defining terms using Atlassian Intelligence in Confluence uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, generating pull request descriptions with Atlassian Intelligence applies the following measures:
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Generate SQL queries in Atlassian Analytics
Generating SQL queries using Atlassian Intelligence in Atlassian Analytics is powered by large language models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and understand natural language, then translates it to structured query language (SQL) within Atlassian Analytics. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Ask Atlassian Intelligence a question in natural language and have it translated into SQL, rather than writing your own SQL queries from scratch. After you ask a question, Atlassian Intelligence uses the Atlassian Data Lake schema of your selected data source to generate an SQL query that can be used to build charts on your Atlassian Analytics dashboards, and can also help you learn about the schema in the Data Lake. We believe that generating SQL queries using Atlassian Intelligence works best in scenarios where:
Not sure what questions to ask?Here are some suggestions:
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It’s important to remember that because of the way that the models used to generate SQL queries using Atlassian Intelligence work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that generating SQL queries using Atlassian Intelligence is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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We understand you may have questions about how generating SQL queries using Atlassian Intelligence uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, generating SQL queries using Atlassian Intelligence applies the following measures.
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Search answers in Confluence using Atlassian Intelligence is powered by LLM models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Knowledge bases are growing too fast for users to keep up. Searching answers in Confluence using Atlassian Intelligence provides a faster path to key information that customers need to move their work forward. This feature helps you easily find the information you need. It understands the types of questions you would ask a teammate, and answers them instantly. Find out more about using Atlassian Intelligence to search for answers in Confluence. We believe that searching answers in Confluence using Atlassian Intelligence works best when your Confluence site is full of detailed, complete, and up-to-date content. This feature does not generate new content, but searches Confluence pages and blogs (while respecting restrictions) to find an answer to your question. Atlassian Intelligence generates answers solely based on what’s in your Confluence, and what you, specifically, have access to. Not sure what questions to ask?Here are some suggestions
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We understand you may have questions about how searching answers in Confluence using Atlassian Intelligence uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, searching answers in Confluence using Atlassian Intelligence applies the following measures:
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Search issues in Jira
Search issues using Atlassian Intelligence in Jira is powered by large language models developed by Open AI. The models include the OpenAI models described here, fine-tuned by Atlassian using generated synthetic data. Atlassian Intelligence uses these models to analyze and understand natural language, then translates it to Jira Query Language (JQL) code within our products. These models generate responses based on your inputs and are probabilistic in nature. This means their responses are generated by predicting the most probable next word or text based on the data they have been trained on. Read more about the capabilities of OpenAI models, and OpenAI fine-tuning. You can also read more about this approach in OpenAI’s research papers. |
You can now ask Atlassian Intelligence what you want in everyday language instead of coming up with complex queries. By searching issues using Atlassian Intelligence, your prompt is translated into a JQL query which quickly assists you in your search for specific issues. We believe searching issues using Atlassian Intelligence works best in scenarios where:
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It's important to remember that because of the way that the models used to search issues using Atlassian Intelligence work, these models can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses you receive might not accurately reflect the content they are based on or include content that sounds reasonable but is false or incomplete. We've found that searching issues using Atlassian Intelligence is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about being as specific as possible in what you ask Atlassian Intelligence to do. Make sure to include the exact fields and values you're looking for. |
We understand you may have questions about how searching issues using Atlassian Intelligence uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, searching issues using Atlassian Intelligence applies the following measures:
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Glean instant insights from your data
Select an Atlassian Intelligence feature below to get a transparent look at use cases and data use.
Chart insights is powered by large language models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated by predicting the most probable next word or text based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Chart insights uses Atlassian Intelligence to help speed up your understanding of data in any chart in Atlassian Analytics. It does so by using the dashboard title, chart title, and chart data (including column headers and row values) to generate a natural language summary of that chart and its data. It will also aim to identify any trends or anomalies to provide you with certain insights into that chart. We believe that Chart insights work best in scenarios where:
Bar charts, line charts, and bar-line charts work best with this feature since they typically have trends, dates, and many rows of data. |
It’s important to remember that because of the way that the models used to power Chart insights work, these models can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that Chart insights is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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We understand you may have questions about how Chart insights uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, Chart insights applies the following measures.
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Suggest request types using Atlassian Intelligence is powered by large language models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze natural language input and generate recommendations for request type names and descriptions for you within Jira Service Management. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
Spend less time figuring out what kind of request types you need to create for your project, and instead get suggestions from Atlassian Intelligence. Simply describe your work and what your team typically manages, to see what types of requests you could create. Select one of the suggestions generated by Atlassian Intelligence to create a request type. Find out more about how to use Atlassian Intelligence to suggest request types. We believe that using Atlassian Intelligence to suggest request types works best in scenarios where:
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It’s important to remember that because of the way that the models used to suggest request types using Atlassian Intelligence work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that using Atlassian Intelligence to suggest request types is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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We understand you may have questions about how using Atlassian Intelligence to suggest request types uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, using Atlassian Intelligence to suggest request types applies the following measures.
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