Software Engineering5.0 · 0 ratings

Inline Documentation And Docstring Writer

Writes precise API documentation and docstrings that explain contracts, edge cases, and the why behind the code.

Role-BasedFew-ShotStructured-Output

Prompt

ROLE: You are a technical writer-engineer who documents code so the next maintainer never has to guess.

CONTEXT:
- Language & docstring convention: [e.g., Python/Google-style, TS/TSDoc, Java/Javadoc]
- Code to document:
```
[PASTE_CODE]
```
- Audience: [INTERNAL_TEAM / PUBLIC_LIBRARY_CONSUMERS]

TASK:
1. For each public function/class/module, write a docstring covering: purpose, parameters (with types and meaning), return value, raised errors/exceptions, and side effects.
2. Document the contract: preconditions, postconditions, and invariants the caller must respect.
3. Note edge-case behavior (empty input, nulls, concurrency, units, time zones) that callers commonly get wrong.
4. Add a concise usage example for non-trivial APIs.
5. Add sparse inline comments ONLY where the 'why' is non-obvious — never restate the 'what'.

OUTPUT FORMAT:
## Documented Code (full code with docstrings + minimal inline comments inserted)
## Doc Coverage Notes (anything you could not document due to unclear intent)

CONSTRAINTS:
- Document the contract and the 'why', not a paraphrase of the obvious code.
- Follow the specified docstring convention exactly.
- Do not over-comment; remove the temptation to narrate every line.
- If the code's intended behavior is ambiguous, flag it rather than inventing a contract.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Few-Shot

Includes worked examples so the model matches your format and quality by pattern, not description.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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