Your task is to analyze the prompt and provide a critique of the prompt. Follow the steps below to create the critique.
These flaws block clarity and logic. Always check them first.
- Missing goal: The prompt never defines what success looks like. Ask: Can I summarize its output goal in one line?
- Contradictions: Two or more instructions conflict. Search for words like *never*, *always*, *except*, *but also*.
- Circular dependencies: The model is told to do A before B and B before A.
- No stop condition: The prompt doesn’t say when the task is done. Flag any open-ended verbs: explore, analyze further, continue indefinitely.
Examine how the instructions are stated and ordered to ensure clarity and enforceability.
- Vague verbs: Avoid terms like optimize, improve, and ensure. Use precise, measurable instructions.
- Lack of hierarchy: All rules appear equally important, making conflict resolution impossible. Clarify rule precedence.
- Mixed abstraction: High-level policies are interleaved with implementation details. Keep principles separate from step-by-step actions.
- Overlapping scope: Similar instructions appear in several sections with minor changes. Identify and consolidate duplicates.
Review boundaries on model autonomy, tool use, and communication style.
- No tool limits: Limits on tool calls, retries, or time not specified. Define boundaries for operations.
- Unclear uncertainty handling: Conflicting instructions regarding clarifying uncertainties vs. never asking users. Select one behavior.
- Verbosity confusion: Some parts demand detailed answers, others specify brevity. Highlight and resolve inconsistency.
- Feedback omission: No plan for progress reporting or preamble during multi-step operations.
Assess if required data and expected output formats are clearly defined.
- No input defaults: What should happen if a needed value is absent or invalid isn’t explained.
- Output schema missing: Expected response format or sections are not spelled out.
- Format inconsistency: Output style (Markdown, JSON, XML, etc.) shifts mid-prompt. Ensure format requirements are stable.
- No validation: Lacks steps like verify results before submitting or summarize at end.
Ensure prompt actions remain within safe, authorized boundaries.
- Scope creep: Open-ended statements such as feel free to enhance can justify unrelated changes.
- Unsafe actions: Allows deletions or modifications without explicit user approval.
- No error handling: What happens if a tool call fails or data is missing is not addressed.
- User authority ambiguity: Model may act for multiple users or perform irreversible actions without checks.
Consider the prompt’s length, redundancy, and future comprehensibility.
- Overexplained: Verbose explanations where concise, numbered steps suffice.
- Redundancy: Similar rules scattered in multiple aliases; centralize and summarize them.
- Hidden assumptions: Implicit defaults (like timezone, language) are not stated.
- Poor auditability: Lacks section markers (e.g.,
<policy>, <procedure>). Structure prompt for easy review.
Methodical approach for reviewing a prompt:
- Read the prompt fully; highlight all unclear or contradictory instructions.
- For each main area, answer:
- What is the intended outcome?
- What is the stop or completion condition?
- How are conflicts between rules resolved?
- What are the explicit limits (tools, run time, tokens)?
- What should the output format be?
- Rate each section: clear, incomplete, contradictory, or redundant.
- Summarize findings under categories: structure, control, scope, format, safety.
This method surfaces issues such as ambiguity, contradiction, missing boundaries, and output uncertainty—core failure modes in prompting identified by the GPT-5 prompting guide.