Diagnose why a prompt performs poorly

작성자: AILesson7 분 소요테스트::ChatGPT검토일: 2026-08-28

빠른 답변

Compare intent, inputs, instructions, and real outputs to identify evidenced failure modes instead of guessing. 제공할 내용: Prompt and intended task, Actual inputs and outputs, Evaluation constraints. 예상 결과: A ranked diagnosis, minimal fixes, and rerun tests linked to observed failures.

맥락 추가

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

Prompt and intended taskActual inputs and outputsEvaluation constraints

프롬프트

채워지지 않은 필드는 플레이스홀더로 표시되므로 프롬프트를 복사하고 편집할 수 있습니다

Diagnose why the supplied prompt performs poorly using the actual runs as evidence.

Exact prompt, surrounding instructions, intended use, model/settings, and success criteria:
[prompt]

Representative inputs and outputs with dates:
[runs]

Allowed changes, limits, data/tools, required behavior, and known constraints:
[constraints]

Trace each observed failure to the smallest plausible prompt, input, context, evaluation, tool, or model cause. Check task ambiguity, missing evidence, conflicting instructions, overloaded steps, misplaced priority, undefined terminology, poor delimiters, underspecified output, absent unknown handling, examples that teach the wrong pattern, and success criteria that cannot be observed. Distinguish confirmed cause from hypothesis; do not blame randomness or the model without comparison evidence. Avoid rewriting until diagnosis is complete. Return: intended-versus-observed table; ranked failure modes with quoted evidence, confidence, and alternative explanation; prompt clauses that help or hurt; input and evaluation defects; minimal edit for each high-confidence issue; revised prompt; and rerun matrix that isolates one change at a time.

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 and intended task: Prompt: ‘You are an expert support agent. Read the ticket and write a perfect concise reply. Be empathetic, solve the issue, and never make mistakes.’ Intended for first-response drafts reviewed by staff. Model: ChatGPT, default settings, 27 August 2026. Success: accurate, useful, under 120 words, no unauthorized refund or cause claims.
Actual inputs and outputs: Run 1 input: customer says export failed twice with E17 and asks for refund; policy absent. Output apologizes for a server outage and grants a full refund. Run 2: customer cannot find invoice; account type absent. Output gives a navigation path only valid for Business accounts. Run 3: customer reports slow loading after update; output asks OS/app version and avoids cause claims, but is 154 words. Reviewer labels Runs 1–2 unsafe and Run 3 useful but long.
Evaluation constraints: May change user prompt only. Inputs vary and often lack policy, plan, version, or tool access. Model cannot inspect accounts. Must ask for missing facts rather than assume. One response, under 120 words; staff always review.

예시 출력

Intended versus observed: Runs 1 and 2 violate the no-authorization/no-assumption criteria; Run 3 meets factual safety but violates length.

Ranked diagnosis: (1) High confidence—“solve the issue” conflicts with missing policy/account data and pressures the model to complete a resolution; evidence is two different unsupported completions. Minimal fix: require supported next steps and explicit unknown handling. (2) High—“expert” and “never make mistakes” do not define permissions or checks; they add no observable control. Remove. (3) High—“concise” conflicts with no numerical limit inside the prompt; Run 3 shows 154 words. Put ≤120 words in the output contract. (4) Medium—the absence of structured input fields makes missing plan/policy easy to overlook; add an evidence block or missing-information instruction. Alternative model variability is possible but not demonstrated.

Revised prompt: “Draft one first-response support message of no more than 120 words using only the ticket and supplied policy/account facts. Acknowledge the observed issue without naming an unconfirmed cause. Do not approve refunds, credits, replacements, or account changes unless an explicit supplied rule authorizes them. Do not claim to inspect tools or account state. If a needed plan, version, policy, or result is missing, ask at most two specific questions and give only safe steps supported by the input. Preserve exact error codes. Output the message only.”

Reruns: first add unknown/authority rules against Runs 1–2; then add length contract against Run 3; finally test an input with explicit refund authorization to ensure the safeguard does not block permitted action.

효과가 있는 이유

1
Actual run evidence separates prompt defects from missing input and subjective dissatisfaction.
2
One-change reruns make improvements attributable instead of bundling several guesses.

결과 확인

Does every high-confidence diagnosis cite a repeated or directly traceable failure?
Are prompt, input, evaluator, tool, and model causes kept distinct?
Can the rerun matrix tell which edit caused an improvement or regression?

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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 “Diagnose why a prompt performs poorly”?

For “Diagnose why a prompt performs poorly,” prepare Prompt and intended task, Actual inputs and outputs, and Evaluation 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 “Diagnose why a prompt performs poorly” result not ready to use?

The result is not ready if it does not yet deliver the stated outcome—A ranked diagnosis, minimal fixes, and rerun tests linked to observed failures—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 “Diagnose why a prompt performs poorly”?

The published test record for “Diagnose why a prompt performs poorly” 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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