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Clarify rate limit error message and actions #43999

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2 changes: 1 addition & 1 deletion content/copilot/how-tos/troubleshoot-copilot/troubleshoot-common-issues.md
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Expand Up @@ -71,7 +71,7 @@ Most people see rate limiting for select models, due to limited capacity.

Service-level request rate limits ensure high service quality for all {% data variables.product.prodname_copilot_short %} users and should not affect typical or even deeply engaged {% data variables.product.prodname_copilot_short %} usage. We are aware of some use cases that are affected by it. {% data variables.product.github %} is iterating on {% data variables.product.prodname_copilot_short %}’s rate-limiting heuristics to ensure it doesn’t block legitimate use cases.

If you are rate limited, the error message will contain the suggested retry time for a successful request. For more information about alternative actions you can take while your limit resets, see [AUTOTITLE](/copilot/concepts/usage-limits#what-to-do-if-you-hit-a-limit).
If you are rate limited, the error message will read: "You've hit your session rate limit. Please upgrade your plan or wait a moment for your limit to reset.". For more information about alternative actions you can take while your limit resets, see [AUTOTITLE](/copilot/concepts/usage-limits#what-to-do-if-you-hit-a-limit).
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Copilot AI Apr 27, 2026

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Quoting an exact, client-specific error string (“will read: …”) is likely to become outdated across Copilot surfaces/versions and may not match what all users see. Consider changing this to non-exact phrasing (e.g., “may say something like…”) or describing the semantics (that the message indicates you’re rate limited and when you can retry), which is more durable and consistent with docs best practices.

Copilot uses AI. Check for mistakes.
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@Joevanca5435 Joevanca5435 Apr 27, 2026

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Responsible use of GitHub Copilot code review

Learn how to use GitHub Copilot code review safely and responsibly by understanding its purposes, capabilities, and limitations.

About GitHub Copilot code review

GitHub Copilot code review is an AI-powered feature that reviews code and provides feedback.

When a user requests a code review from Copilot, Copilot scans through the code changes, plus additional relevant context, and provides feedback on the code. As part of that feedback, it may also provide specific suggested code changes.

Copilot's review can be customized with custom instructions, which are natural language descriptions of coding style and best practices. For more information, see Adding repository custom instructions for GitHub Copilot.

GitHub Copilot code review inspects your code and provides feedback using a combination of natural language processing and machine learning. This process can be broken down into a number of steps.

Input processing

The code changes are combined with other relevant, contextual information (for example, the pull request’s title and body on GitHub), and any custom instructions that have been defined, to form a prompt, and that prompt is sent to a large language model.

Language model analysis

The prompt is then passed through the Copilot language model, which is a neural network that has been trained on a large body of text data. The language model analyzes the input prompt.

Response generation

The language model generates a response based on its analysis of the input prompt. This response can take the form of natural language suggestions and code suggestions.

Output formatting

The response generated by Copilot is presented to the user either directly in the supported editor, or as a pull request review on GitHub, providing code feedback linked to specific lines of specific files.

Where Copilot has provided a code suggestion, the suggestion is presented as a suggested change, which can be applied with a couple of clicks.

Model usage

Copilot code review is a purpose-built product that uses a carefully tuned mix of models, prompts, and system behaviors to deliver consistent, high-quality feedback across a wide range of codebases. Model switching is not supported, as changing the model is likely to compromise reliability, user experience, and the quality of review comments. Each use of this feature consumes one premium request. See Requests in GitHub Copilot.

Note

Copilot code review may use models that are not enabled on your organization's "Models" settings page. The "Models" settings page only controls Copilot Chat.

Since Copilot code review is generally available, all model usage will be subject to the generally available terms. See Managing policies and features for GitHub Copilot in your organization.

Use case for GitHub Copilot code review

The goal of GitHub Copilot code review is to quickly provide feedback on a developer’s code. This can enable developers to get code ready to merge more quickly and increase overall code quality.

Improving the performance of GitHub Copilot code review

Use Copilot code review to supplement human reviews, not to replace them

While GitHub Copilot code review can be a powerful tool for improving code quality, it is important to use it as a tool, rather than to replace human reviews.

You should always review and verify the feedback generated by Copilot code review, and supplement Copilot's feedback with careful human review to ensure your code meets your requirements.

Provide feedback

If you encounter any issues or limitations with Copilot code review, we recommend that you provide feedback by using the thumbs up and thumbs down buttons on Copilot's comments. This can help GitHub to improve the tool and address any concerns or limitations.

Custom instructions

You can configure custom instructions to help Copilot understand your coding style and best practices. For more information, see Adding repository custom instructions for GitHub Copilot.

Limitations of GitHub Copilot code review

Depending on factors such as your codebase and programming language, you may encounter different levels of performance when using GitHub Copilot code review. The following information is designed to help you understand system limitations and key concepts about performance as they apply to GitHub Copilot code review.

Missed code quality problems

Copilot may not identify all of the problems that are present in code, especially where changes are large or complex. To ensure that all relevant problems are identified and corrected, Copilot code review should be supplemented with careful human code review.

False positives

Copilot code review has a risk of "hallucination" - that is, it may highlight problems in reviewed code that do not exist or are based on misunderstandings of the code. Comments generated by Copilot code review should be carefully reviewed and considered before taking action and making changes.

Inaccurate or insecure code

As part of its comments, Copilot code review may provide specific code suggestions. The code generated may appear to be valid, but may not actually be semantically or syntactically correct, or may not correctly resolve the problem identified in the comment. In addition, code generated by Copilot may contain security vulnerabilities or other issues. You should always carefully review and test code generated by Copilot.

Potential biases

Copilot's training data is drawn from existing code repositories, which may contain biases and errors that can be perpetuated by the tool. Additionally, Copilot code review may be biased toward certain programming languages or coding styles, which can lead to suboptimal or incomplete feedback.

Next steps

For details of how to use Copilot code review, see:

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Copilot AI Apr 27, 2026

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The punctuation around the quoted message creates an awkward double-termination (reset.\".) because the quoted string already ends with a period and the sentence adds another. Rephrase to avoid consecutive sentence punctuation (e.g., end the sentence right after the quote, or remove terminal punctuation inside the quote), and consider formatting the message as inline code or a blockquote to improve readability.

Copilot uses AI. Check for mistakes.
Copy link
Copy Markdown

@Joevanca5435 Joevanca5435 Apr 27, 2026

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Responsible use of GitHub Copilot code review

Learn how to use GitHub Copilot code review safely and responsibly by understanding its purposes, capabilities, and limitations.

About GitHub Copilot code review

GitHub Copilot code review is an AI-powered feature that reviews code and provides feedback.

When a user requests a code review from Copilot, Copilot scans through the code changes, plus additional relevant context, and provides feedback on the code. As part of that feedback, it may also provide specific suggested code changes.

Copilot's review can be customized with custom instructions, which are natural language descriptions of coding style and best practices. For more information, see Adding repository custom instructions for GitHub Copilot.

GitHub Copilot code review inspects your code and provides feedback using a combination of natural language processing and machine learning. This process can be broken down into a number of steps.

Input processing

The code changes are combined with other relevant, contextual information (for example, the pull request’s title and body on GitHub), and any custom instructions that have been defined, to form a prompt, and that prompt is sent to a large language model.

Language model analysis

The prompt is then passed through the Copilot language model, which is a neural network that has been trained on a large body of text data. The language model analyzes the input prompt.

Response generation

The language model generates a response based on its analysis of the input prompt. This response can take the form of natural language suggestions and code suggestions.

Output formatting

The response generated by Copilot is presented to the user either directly in the supported editor, or as a pull request review on GitHub, providing code feedback linked to specific lines of specific files.

Where Copilot has provided a code suggestion, the suggestion is presented as a suggested change, which can be applied with a couple of clicks.

Model usage

Copilot code review is a purpose-built product that uses a carefully tuned mix of models, prompts, and system behaviors to deliver consistent, high-quality feedback across a wide range of codebases. Model switching is not supported, as changing the model is likely to compromise reliability, user experience, and the quality of review comments. Each use of this feature consumes one premium request. See Requests in GitHub Copilot.

Note

Copilot code review may use models that are not enabled on your organization's "Models" settings page. The "Models" settings page only controls Copilot Chat.

Since Copilot code review is generally available, all model usage will be subject to the generally available terms. See Managing policies and features for GitHub Copilot in your organization.

Use case for GitHub Copilot code review

The goal of GitHub Copilot code review is to quickly provide feedback on a developer’s code. This can enable developers to get code ready to merge more quickly and increase overall code quality.

Improving the performance of GitHub Copilot code review

Use Copilot code review to supplement human reviews, not to replace them

While GitHub Copilot code review can be a powerful tool for improving code quality, it is important to use it as a tool, rather than to replace human reviews.

You should always review and verify the feedback generated by Copilot code review, and supplement Copilot's feedback with careful human review to ensure your code meets your requirements.

Provide feedback

If you encounter any issues or limitations with Copilot code review, we recommend that you provide feedback by using the thumbs up and thumbs down buttons on Copilot's comments. This can help GitHub to improve the tool and address any concerns or limitations.

Custom instructions

You can configure custom instructions to help Copilot understand your coding style and best practices. For more information, see Adding repository custom instructions for GitHub Copilot.

Limitations of GitHub Copilot code review

Depending on factors such as your codebase and programming language, you may encounter different levels of performance when using GitHub Copilot code review. The following information is designed to help you understand system limitations and key concepts about performance as they apply to GitHub Copilot code review.

Missed code quality problems

Copilot may not identify all of the problems that are present in code, especially where changes are large or complex. To ensure that all relevant problems are identified and corrected, Copilot code review should be supplemented with careful human code review.

False positives

Copilot code review has a risk of "hallucination" - that is, it may highlight problems in reviewed code that do not exist or are based on misunderstandings of the code. Comments generated by Copilot code review should be carefully reviewed and considered before taking action and making changes.

Inaccurate or insecure code

As part of its comments, Copilot code review may provide specific code suggestions. The code generated may appear to be valid, but may not actually be semantically or syntactically correct, or may not correctly resolve the problem identified in the comment. In addition, code generated by Copilot may contain security vulnerabilities or other issues. You should always carefully review and test code generated by Copilot.

Potential biases

Copilot's training data is drawn from existing code repositories, which may contain biases and errors that can be perpetuated by the tool. Additionally, Copilot code review may be biased toward certain programming languages or coding styles, which can lead to suboptimal or incomplete feedback.

Next steps

For details of how to use Copilot code review, see:


In case you experience repeated rate limiting in {% data variables.product.prodname_copilot_short %} contact {% data variables.contact.contact_support_page %}.

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