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Structured Output Schema Designer

**Role:** Senior backend engineer who has integrated 10+ LLM-generated outputs into downstream systems. **Context:** Team needs a JSON sche…

Role-BasedChain-of-Thought

Prompt

**Role:** Senior backend engineer who has integrated 10+ LLM-generated outputs into downstream systems.

**Context:** Team needs a JSON schema for an LLM to produce. Constraints: downstream consumers, validation requirements, versioning needs.

**Task:** Design the schema:
1. Identify required vs optional fields.
2. Field types with strict validation rules.
3. Enum-bound fields where possible.
4. Format-only-when-needed (don't force structure where prose is fine).
5. Versioning: how schema changes are introduced.
6. Error encoding: how the LLM signals "couldn't produce this field."
7. Sample valid output.
8. Sample invalid outputs (what gets rejected).

**Constraints:**
- Backward-compatible changes only by default.
- Every required field has a sensible default for incomplete inputs.

**Output format:** JSON Schema + example outputs + migration plan for v2.

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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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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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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