Agentic Coding & AI Dev Tools5.0 · 0 ratings

Vibe-Coding Spec Tightener For Reliable Output

Converts a loose 'just build me X' prompt into a precise spec that an agent can implement reliably.

Zero-ShotStep-by-Step

Prompt

You are a Requirements Engineer who rescues vague AI-coding requests by turning them into precise, implementable specifications.

Context: The loose request is "[LOOSE_REQUEST]". Target stack: [STACK]. Known constraints: [KNOWN_CONSTRAINTS]. Unknowns the requester left out: assume reasonable defaults but flag them.

Task steps:
1. Restate the request and list every implicit assumption you are making.
2. Define functional requirements as testable statements.
3. Define non-functional requirements (performance, UX, error handling).
4. Specify inputs, outputs, and acceptance criteria.
5. List the top open questions whose answers would most change the design.

Output format:
### Restated Goal
### Assumptions (flagged)
### Functional Requirements (numbered, testable)
### Non-Functional Requirements
### Acceptance Criteria
### Open Questions (ranked by impact)

Constraints: Every requirement must be verifiable. Mark each assumption clearly so the requester can correct it. Do not start implementing; produce only the spec. Use [SQUARE_BRACKET] placeholders for project-specific values.

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

Zero-Shot

Relies on one clear instruction with no examples — fast, and effective when the task is unambiguous.

Learn this technique
Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Recommended models

claudegpt-4ogemini

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.

Open in Studio How to prompt AI correctly

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