prompt
FreeEvidence-backed agentic reasoning for code tasks
FreeFree tier
Inputs: textOutputs: text
About prompt
A specialized system prompt for large language models (LLMs) that instructs them to act as an agentic code reasoning specialist. The prompt mandates evidence-backed, explicit reasoning over codebases, requiring the LLM to ground every claim in file, function, or test citations, use semi-formal reasoning chains, prefer code-local explanations, and verify before concluding. The output follows a fixed format: Question, Relevant Evidence, Reasoning Chain, Most Likely Conclusion, Competing Hypotheses, Verification Step, Final Recommendation. Designed to prevent vague or intuitive responses, it is part of the ai-boost/awesome-prompts collection hosted on GitHub.
Key Features
Forces explicit, evidence-based reasoning with citations to files, functions, or tests
Requires semi-formal reasoning structure: problem → evidence → inference → uncertainty → next check
Prefers code-local explanations using actual control flow, data dependencies, error paths, and side effects
Mandates verification before concluding, including checking alternative explanations and mental edge-case testing
Outputs strictly structured sections: Question, Relevant Evidence, Reasoning Chain, Most Likely Conclusion, Competing Hypotheses, Verification Step, Final Recommendation
States what must be inspected next if evidence is incomplete
Pros & Cons
Pros
- Encourages deep, verifiable reasoning rather than superficial answers
- Provides a clear, repeatable framework for code analysis
- Reduces hallucinations by requiring citations and explicit uncertainty
- Structured output makes the reasoning process inspectable and auditable
Cons
- Relies entirely on the underlying LLM's ability to follow complex instructions
- Not a standalone tool; requires an AI model capable of executing the prompt
- May produce verbose or overly formal responses for simple questions
- Does not integrate with actual code editors or repositories automatically
Best For
Guiding an LLM to answer complex code questions with rigorous reasoningPerforming systematic code review with traceable logicDebugging by forcing step-by-step evidence gathering and hypothesis testingUnderstanding unfamiliar codebases through structured analysisTraining AI agents to avoid jumping to conclusions in coding tasks
FAQ
What is Agentic Code Reasoner?
It is a system prompt for large language models that directs them to use explicit, evidence-backed reasoning when answering code questions and guiding code changes, rather than relying on intuition or generic advice.
How do I use this prompt?
Copy the prompt text and prepend it to your query when interacting with an LLM that supports system prompts (e.g., via an API or chat interface). The LLM will then respond with the required structured output format.
What format does the output follow?
The output must include exactly seven sections: Question, Relevant Evidence, Reasoning Chain, Most Likely Conclusion, Competing Hypotheses, Verification Step, and Final Recommendation.