S'inscrire gratuitement

Diagnose why a prompt performs poorly

Auteur: AILesson7 min de préparationTesté avec:ChatGPTRévisé: 2026-08-28

Réponse rapide

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

1

Ajouter votre contexte

Votre texte reste dans ce navigateur. AILesson Prompts ne l’envoie ni à un modèle ni à un serveur.

2

Votre prompt

Les champs non remplis restent visibles sous forme d’espaces réservés, afin que vous puissiez quand même copier et modifier le prompt.

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.
Essayer dans le Playground
Privé par défautL’assemblage du prompt se fait localement dans votre navigateur. Évitez de placer des informations confidentielles dans un service d’IA, sauf si votre organisation l’autorise.

De l'entrée au résultat

Un exemple détaillé

Voyez comment un contexte concret transforme cette recette en résultat utilisable.

Entrée réelle

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.

Exemple de sortie

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.

Pourquoi cela fonctionne

  1. 1

    Actual run evidence separates prompt defects from missing input and subjective dissatisfaction.

  2. 2

    One-change reruns make improvements attributable instead of bundling several guesses.

Vérifier le résultat

  • 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?

Utilisez-la en toute confiance

Questions fréquentes

Des réponses pratiques sur le bon moment pour utiliser cette recette, ce qu’il faut fournir et les cas où une vérification humaine reste nécessaire.

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.

Plus de façons d'explorer

Où se situe cette recette

Prompts texteRetours et relectureConception de promptsPour les rédacteurs

Faites avancer votre travail

AILesson · Cours recommandés

Votre prochaine étape : mettez l’IA à contribution

Passez de la compréhension de l'IA à l'action. Entraînez-vous à formuler des requêtes, à évaluer et à améliorer les résultats grâce à des leçons interactives pour le travail et la vie quotidienne.

Découvrir tous les cours
Image de couverture de L’IA pour débutants

15 leçons interactives

L’IA pour débutants

Lancez une conversation utile avec une IA, améliorez sa réponse, vérifiez l’essentiel et sachez quand c’est à vous d’agir.

Découvrir ce cours
Image de couverture de Les bases de ChatGPT

10 leçons interactives

Les bases de ChatGPT

Découvrez ChatGPT à travers trois flux de travail pratiques : mener une première conversation, travailler à partir d’un fichier, puis utiliser Projects pour réutiliser un plan vérifié dans une nouvelle conversation.

Découvrir ce cours
Image de couverture de Comment fonctionne l’IA générative

7 leçons interactives

Comment fonctionne l’IA générative

Comprenez comment les systèmes courants de chat IA apprennent des structures linguistiques, utilisent les messages actuels, construisent leurs réponses et intègrent des sources et des outils.

Découvrir ce cours