prompt-review

To review AI prompt quality within source code, scan the codebase to discover prompt patterns and grade them against a framework assessing clarity, specificity, context, structure, robustness, and efficiency. This process identifies anti-patterns and generates concrete rewrite suggestions with estimated token savings.

Prompt injection is an anti-pattern where malicious inputs manipulate LLM behavior. Detect it by scanning source code prompts against a robustness framework that flags these vulnerabilities, ensuring your application's system prompts and user messages resist unintended overrides.

Reduce LLM token usage and costs by analyzing prompts for efficiency, identifying verbose or unclear patterns, and applying concrete rewrite suggestions. This optimization process estimates the token savings impact for each recommended prompt improvement.

The best way to evaluate system prompts and few-shot examples is to scan source code to discover all AI prompt patterns, then apply a 6-dimension quality grading framework. This generates actionable code diffs and prioritized recommendations for improving application prompts.

You can get actionable code diffs for improving LLM prompts by running an automated review that discovers prompt patterns within source code. The analysis grades prompt quality and generates concrete, prioritized rewrite suggestions with estimated impact and token savings.

Your AI chatbot misinterprets queries due to low prompt clarity, specificity, or context. Fix it by reviewing the prompts against a 6-dimension quality framework, flagging anti-patterns, and applying suggested rewrites to ensure accurate understanding and efficient responses.