Define a verifiable output format for a prompt

작성자: AILesson6 분 소요테스트::ChatGPT검토일: 2026-08-28

빠른 답변

Convert vague output wishes into fields, types, statuses, constraints, and validation rules. 제공할 내용: Prompt task and downstream use, Current output request and examples, Format and validation constraints. 예상 결과: An output contract with schema, human-readable example, empty states, and validation checks.

맥락 추가

텍스트는 이 브라우저에 유지됩니다. AILesson Prompts는 이를 모델이나 서버로 보내지 않습니다.

Prompt task and downstream useCurrent output request and examplesFormat and validation constraints

프롬프트

채워지지 않은 필드는 플레이스홀더로 표시되므로 프롬프트를 복사하고 편집할 수 있습니다

Design a verifiable output contract for the prompt task.

Input, transformation, reader/system, decision, and failure consequences:
[task]

Current format wording, desired examples, rejected outputs, and used fields:
[output]

Serialization, parser limits, length, locale, ordering, required/optional fields, and review:
[constraints]

Define the smallest output structure that supports the downstream use. For every field specify name, purpose, type, required status, allowed values or pattern, units, evidence link, unknown/empty representation, and cross-field rules. Separate data from commentary and machine fields from reviewer notes. Avoid asking for mutually incompatible formats, ornamental sections, or prose inside fields meant for parsing. Include deterministic ordering where relevant. Do not use null, empty string, zero, false, and “unknown” interchangeably. Return: output-design rationale; formal or tabular schema; exact prompt clause; valid complete example; valid empty/unknown example; invalid examples with reasons; validation checklist or pseudocode; and human-review fields that cannot be mechanically verified.

Playground에서 사용해 보기

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입력에서 결과까지

적용 예시

구체적인 맥락이 이 레시피를 바로 사용할 수 있는 결과로 바꾸는 방법을 확인하세요

실제 입력

Prompt task and downstream use: Extract explicit commitments from a meeting transcript. A workflow imports the result into a task-review screen; no task is created automatically. Reviewers need action, owner, due date, status, and evidence locator. Missing owners/dates must remain visible. Treat suggestions as non-commitments. A wrong owner or invented due date creates accountability risk.
Current output request and examples: Current instruction says ‘Give me a clean structured list.’ Good output had one row per commitment and timestamps. Rejected outputs mixed a summary with bullets, used ‘soon’ as a due date, and omitted owner when absent. Reviewers do not use priority or sentiment.
Format and validation constraints: Strict JSON, no Markdown fences or extra prose. Parser accepts UTF-8 JSON array. ISO 8601 dates only when exact calendar date is stated. Required keys in fixed order for snapshots. Status values proposed, confirmed, completed, cancelled. Evidence contains speaker and timestamp. Human reviews commitment classification and identity.

예시 출력

Design: one array item per explicit commitment; omit summaries, priority, and sentiment. Unknown owner/date use null because absence is meaningful; an empty string is invalid. “Soon” remains in evidence but cannot populate due_date.

Schema: action string, required, nonblank; owner string|null, required key; due_date string|null, required key, YYYY-MM-DD only; status enum, required; evidence object, required, with speaker nonblank string, timestamp HH:MM:SS, excerpt nonblank string ≤200 characters. Key order: action, owner, due_date, status, evidence. Cross-field rule: completed status requires evidence that completion was stated; due_date must be null unless an exact date is in evidence.

Exact prompt clause: “Return only a UTF-8 JSON array. Each explicit commitment is one object with keys in this order: action, owner, due_date, status, evidence. Use null for an unstated owner or exact due date; never omit keys or convert relative wording to a date. status must be proposed, confirmed, completed, or cancelled. evidence must contain speaker, HH:MM:SS timestamp, and a source excerpt no longer than 200 characters. Return [] when no explicit commitment exists. Add no prose or Markdown.”

Valid: [{"action":"Send the revised budget","owner":"Nora","due_date":null,"status":"confirmed","evidence":{"speaker":"Nora","timestamp":"00:14:22","excerpt":"I’ll send the revised budget soon."} }]. Valid empty: []. Invalid: due_date "soon" fails ISO rule; omitted owner fails required-key rule. Human review still verifies whether the excerpt is a commitment and whether “Nora” resolves to the correct person.

효과가 있는 이유

1
Typed fields and explicit empty states turn output quality into observable rules.
2
Cross-field validation catches internally inconsistent outputs that look well formatted.

결과 확인

Does every field serve a real downstream decision, display, or check?
Are unknown, absent, not applicable, and zero values unambiguous?
Can mechanical checks and human judgment responsibilities be distinguished?

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자주 묻는 질문

이 레시피를 언제 사용해야 하는지, 무엇을 제공해야 하는지, 그리고 어떤 부분에서 사람의 검토가 여전히 중요한지에 대한 실용적인 답변

What should I prepare before using “Define a verifiable output format for a prompt”?

For “Define a verifiable output format for a prompt,” prepare Prompt task and downstream use, Current output request and examples, and Format and validation constraints. Replace placeholders only with information you can verify. If a detail is unknown, preserve that uncertainty explicitly instead of asking the model to infer it.

When is the “Define a verifiable output format for a prompt” result not ready to use?

The result is not ready if it does not yet deliver the stated outcome—An output contract with schema, human-readable example, empty states, and validation checks—from the supplied evidence, or if it relies on unresolved assumptions, missing approvals, or invented details. Use the checks as release gates: revise the source inputs or assign a named, authorized reviewer instead of polishing an unsupported output.

Which AI tools have recorded tests for “Define a verifiable output format for a prompt”?

The published test record for “Define a verifiable output format for a prompt” lists ChatGPT as of 2026-08-28. This confirms recorded runs, not guaranteed compatibility or identical results in later product versions. For another tool or version, keep every constraint visible and repeat the result checks before use.

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