refine-aichat-role

To refine AI role prompts, you apply structured editing guidelines to the role file, such as adding explicit WRONG/RIGHT examples and wrapping identifiers in backticks. This targets the most impactful changes to align generation behavior with user expectations.

AI chat roles generate unexpected outputs when the prompt lacks recency bias or explicit examples. Nudging critical rules to the end of the role file and providing template-driven formatting ensures robust, maintainable behavior for Haiku-class models.

The best way to fix an AI chat role prompt is through rule-based refinement targeting specific divergences. Edit the role file to apply guidelines like recency bias and explicit WRONG/RIGHT examples, ensuring the output meets your requirements.

You edit aichat role files by applying targeted refinements based on user feedback. This involves incorporating template-driven formatting, ensuring identifiers like file names are wrapped in backticks, and structuring the prompt for maintainability.

Prompt engineering with recency bias works effectively for Haiku-class models by nudging critical rules to the end of the role file. This guides the model to prioritize the most recent instructions, improving output quality and alignment.

You should avoid template-driven formatting for role prompts when the generation task requires highly dynamic or unstructured outputs. However, for committer and pr-writer roles, maintaining structured formatting ensures robust and maintainable behavior.