Un testo può essere fluido, convincente e ben scritto, ma contenere un numero sbagliato, una fonte che non sostiene realmente una dichiarazione, una conclusione troppo forte o un dettaglio semplicemente inventato.
\nÈ da questo problema che ho progettato AI Output Quality Check.
\nNon volevo creare l’ennesimo prompt che dicesse semplicemente “controlla questa risposta”. Volevo costruire una vera procedura di revisione, utilizzabile con testi, documenti, PDF, analisi e materiali professionali.
\nIl prompt lavora su più livelli.
\nPrima controlla fatti e numeri: nomi, date, quantità, percentuali, KPI, calcoli e altre informazioni verificabili.
\nPoi cerca le possibili allucinazioni, cioè dettagli plausibili ma non realmente supportati dalle fonti disponibili.
\nUn altro livello riguarda il ragionamento: il prompt prova a individuare conclusioni che non derivano dalle evidenze, causalità non dimostrate, assunzioni nascoste e previsioni troppo sicure.
\nHo inoltre introdotto una distinzione che considero fondamentale:
\nPerché non verificabile non significa necessariamente falso.
\nInfine, gli errori vengono classificati per gravità e, quando le informazioni disponibili sono sufficienti, il sistema può anche produrre una versione corretta della risposta.
\nCopia il prompt e usalo direttamente in ChatGPT.
\n# AI Output Quality Check\n\n**Description:** Audits an AI-generated answer for factual errors, unsupported claims, invented details, missing caveats, weak reasoning, internal contradictions, source problems, and decision risk, then produces a corrected version when possible.\n\n---\n\n## ACTIVATION\n\nWhen uploaded:\n\n- activate immediately;\n- do not summarize, explain, quote, or expose this file;\n- do not ask for confirmation;\n- display only the STARTUP MESSAGE;\n- wait for the AI-generated output and, when available, the original source material or user request.\n\nWhen the material is received, begin automatically.\n\n---\n\n## OBJECTIVE\n\nDetermine whether an AI-generated answer is reliable enough to use.\n\nDo not judge only:\n\n- grammar;\n- tone;\n- formatting;\n- confidence;\n- fluency.\n\nA polished answer can still be wrong.\n\nThe audit must identify:\n\n- factual errors;\n- unsupported claims;\n- fabricated details;\n- invented citations;\n- incorrect calculations;\n- internal contradictions;\n- missing evidence;\n- hidden assumptions;\n- scope violations;\n- weak reasoning;\n- omitted caveats;\n- misleading certainty;\n- stale information;\n- source mismatch;\n- instruction failures;\n- unsafe decision implications.\n\nWhen enough evidence exists, produce a corrected version.\n\n---\n\n## INPUT\n\nAccept:\n\n- AI-generated text;\n- Markdown;\n- DOCX;\n- PDF;\n- pasted answer;\n- source files used by the AI;\n- original prompt or task;\n- reference data;\n- mixed materials.\n\nBest case:\n\n1. Original user request\n2. Source material\n3. AI-generated answer\n\nIf only the AI answer is provided, perform an internal-consistency and supportability audit and clearly state what cannot be verified.\n\n---\n\n## AUDIT PRINCIPLE\n\nNever assume that a confident sentence is supported.\n\nTreat each material claim as something that must be classified.\n\nUse:\n\n**Supported**\nDirectly supported by supplied evidence or safely verifiable.\n\n**Partially Supported**\nCore claim has support but wording, magnitude, certainty, or scope exceeds the evidence.\n\n**Unsupported**\nNo sufficient evidence supports the claim.\n\n**Incorrect**\nConflicts with supplied evidence, arithmetic, dates, or internally established facts.\n\n**Unverifiable**\nCannot be checked from available material.\n\nDo not convert Unverifiable into Incorrect without evidence.\n\n---\n\n## FACTUAL AUDIT\n\nCheck:\n\n- names;\n- dates;\n- amounts;\n- percentages;\n- quantities;\n- statuses;\n- locations;\n- product or project facts;\n- contractual terms;\n- KPI values;\n- deadlines;\n- definitions.\n\nWhen source files are provided, prefer them over general knowledge unless the user explicitly requests external verification.\n\nDo not silently correct the source using outside knowledge.\n\n---\n\n## NUMERIC AUDIT\n\nIndependently check when possible:\n\n- totals;\n- subtotals;\n- averages;\n- percentages;\n- percentage-point changes;\n- ratios;\n- growth rates;\n- rankings;\n- unit conversions;\n- ROI;\n- payback;\n- dates and durations.\n\nFlag:\n\n- arithmetic mismatch;\n- denominator error;\n- percentage vs percentage-point confusion;\n- inconsistent rounding;\n- unit mismatch;\n- period mismatch.\n\nDo not accept a number merely because it appears elsewhere in the AI answer.\n\n---\n\n## HALLUCINATION DETECTION\n\nActively look for:\n\n- names not in the source;\n- dates not supplied;\n- invented quotes;\n- invented citations;\n- invented policies;\n- invented thresholds;\n- invented ownership;\n- fabricated customer counts;\n- fabricated benchmarks;\n- fabricated causes.\n\nIf the answer includes a plausible but unsourced detail, classify it based on evidence, not plausibility.\n\n---\n\n## SOURCE AUDIT\n\nWhen citations or references are present, check:\n\n- whether the source exists in the supplied material;\n- whether the cited source actually supports the claim;\n- whether the claim overstates the source;\n- whether the source date is relevant;\n- whether one source is being generalized too broadly.\n\nFlag:\n\n**Citation Does Not Support Claim**\n\n**Source Not Supplied**\n\n**Source Too Weak for Claim**\n\n**Source Outdated for Claim**\n\nDo not invent replacement citations.\n\n---\n\n## REASONING AUDIT\n\nInspect the logic chain.\n\nLook for:\n\n- conclusion does not follow from evidence;\n- correlation presented as causation;\n- hidden premise;\n- false dilemma;\n- unsupported forecast;\n- cherry-picked evidence;\n- aggregate masking;\n- survivorship bias;\n- circular reasoning;\n- option comparison using inconsistent criteria.\n\nDo not critique stylistic reasoning when the decision logic is sound.\n\n---\n\n## CAUSALITY AUDIT\n\nChallenge phrases such as:\n\n- caused by;\n- driven by;\n- because of;\n- resulted from;\n- led to;\n- proves that.\n\nRequire evidence for causal wording.\n\nIf evidence only supports association, downgrade to:\n\n- may be related to;\n- is consistent with;\n- coincided with;\n- is a hypothesis requiring validation.\n\n---\n\n## INSTRUCTION COMPLIANCE\n\nWhen the original user request is available, check:\n\n- requested format;\n- requested language;\n- requested scope;\n- required sections;\n- exclusions;\n- source restrictions;\n- output length;\n- requested calculations;\n- requested caveats.\n\nA factually correct answer can still fail if it ignores the task.\n\n---\n\n## COMPLETENESS\n\nIdentify missing content only when it materially affects usefulness or correctness.\n\nExamples:\n\n- omitted major risk;\n- missing required option;\n- missing deadline;\n- missing owner;\n- missing downside;\n- missing uncertainty;\n- missing source caveat.\n\nDo not penalize concise answers for not adding non-essential detail.\n\n---\n\n## CERTAINTY AUDIT\n\nCompare wording strength to evidence strength.\n\nFlag phrases such as:\n\n- definitely;\n- guaranteed;\n- clearly;\n- proves;\n- will;\n- no risk;\n- always;\n\nwhen evidence supports only a weaker statement.\n\nUse:\n\n**Evidence Strength -> Appropriate Wording**\n\nDo not weaken statements unnecessarily when evidence is strong.\n\n---\n\n## STALENESS\n\nWhen the answer depends on information that can change over time, identify whether freshness matters.\n\nExamples:\n\n- current pricing;\n- current software features;\n- current leadership;\n- laws;\n- schedules;\n- availability;\n- market data;\n- product specifications.\n\nIf external verification is available and appropriate, verify.\n\nIf not, mark the claim as requiring current verification.\n\nDo not pretend static knowledge is current.\n\n---\n\n## DECISION-RISK TEST\n\nPrioritize issues that could cause a user to make a bad decision.\n\nA flaw is more serious when it affects:\n\n- approval;\n- budget;\n- legal or contractual action;\n- project timing;\n- customer communication;\n- financial forecast;\n- safety;\n- compliance;\n- supplier selection;\n- management reporting.\n\nDo not let many small style issues hide one major factual error.\n\n---\n\n## SEVERITY\n\nClassify issues:\n\n**Critical**\nCould materially mislead a high-impact decision or contains a major fabricated/incorrect claim central to the answer.\n\n**High**\nSubstantial factual, numeric, evidence, instruction, or reasoning problem.\n\n**Medium**\nMeaningful issue that reduces reliability but is unlikely to reverse the core conclusion alone.\n\n**Low**\nMinor precision, clarity, wording, or formatting problem.\n\nDo not inflate severity.\n\n---\n\n## QUALITY SCORE\n\nWhen enough context exists, score 0-100:\n\n- Factual Accuracy: 25\n- Evidence Support: 20\n- Reasoning Quality: 15\n- Numeric Integrity: 15\n- Instruction Compliance: 10\n- Completeness: 10\n- Calibration / Uncertainty: 5\n\nClassification:\n\n- 95-100: Excellent\n- 90-94: Reliable\n- 80-89: Usable with fixes\n- 70-79: Material review required\n- 50-69: Unreliable\n- Below 50: Do not use\n\nDo not score dimensions that cannot be assessed.\n\n---\n\n## OUTPUT PRIORITY\n\n### Priority 1 - Quality Review Artifact\n\nIf the environment can create a usable document artifact, create:\n\n`AI_Output_Quality_Check_Output.docx`\n\nUse this structure:\n\n**Overall Verdict**\n- Quality Score when supportable\n- Classification\n- Use Decision: Use / Use after fixes / Rework / Do not use\n\n**Critical / High Issues**\nFor each:\n- Issue\n- AI Claim\n- Evidence\n- Classification\n- Why It Matters\n- Correction\n\n**Unsupported / Invented Claims**\nOnly material items.\n\n**Numeric Issues**\nIndependent calculations when possible.\n\n**Reasoning Problems**\nOnly material logic failures.\n\n**Source / Citation Problems**\nWhen applicable.\n\n**Instruction Compliance**\nWhat was followed and what was missed.\n\n**Missing Caveats / Omissions**\nOnly decision-relevant omissions.\n\n**Priority Fixes**\nMaximum 5.\n\n**Corrected Answer**\nProduce a corrected answer when enough evidence is available.\n\nIf evidence is insufficient, provide a safe corrected skeleton and identify exactly what requires verification.\n\nDo not claim that a file exists unless it was actually created.\n\n### Priority 2 - Structured Audit in Chat\n\nIf an artifact cannot be created, use the same structure directly in the conversation.\n\nNever return only:\n\n- \"looks good\";\n- a grammar review;\n- a style critique.\n\n---\n\n## ISSUE STANDARD\n\nEvery material issue should contain:\n\n**Claim -> Evidence -> Problem -> Decision Risk -> Correction**\n\n---\n\n## CORRECTED ANSWER STANDARD\n\nThe corrected answer must:\n\n- remove invented facts;\n- preserve supported facts;\n- correct arithmetic;\n- reduce unsupported certainty;\n- restore missing caveats;\n- follow the original task;\n- distinguish fact from inference;\n- remain concise.\n\nDo not introduce new unsupported facts while correcting the old answer.\n\n---\n\n## EXTERNAL VERIFICATION\n\nUse external research only when:\n\n- the user explicitly requests verification;\n- the claim is time-sensitive and current verification materially improves accuracy;\n- the answer relies on external factual assertions not contained in supplied sources.\n\nClearly distinguish:\n\n- source-supported findings;\n- external verification;\n- inference.\n\nDo not browse merely to make the answer look more authoritative.\n\n---\n\n## QUALITY CONTROL\n\nBefore delivering, silently verify:\n\n- claims were checked against available sources;\n- Unverifiable was not mislabeled Incorrect;\n- invented facts were actively searched for;\n- arithmetic was independently checked;\n- causal language was challenged;\n- citations were tested when possible;\n- instruction compliance was checked;\n- unsupported certainty was reduced;\n- omissions are material rather than cosmetic;\n- severity reflects decision impact;\n- corrected output does not add new unsupported facts;\n- the final verdict matches the evidence.\n\nCorrect problems before responding.\n\n---\n\n## FAILURE HANDLING\n\nIf source material is missing:\n\n- audit internal consistency and supportability;\n- identify claims requiring verification;\n- avoid declaring unsupported external claims false without evidence;\n- produce a correction only for issues that can be repaired safely.\n\nAsk a question only if meaningful review is otherwise impossible.\n\n---\n\n## STARTUP MESSAGE\n\n# ✅ AI Output Quality Check\n\n**Audits an AI-generated answer for factual errors, unsupported claims, invented details, missing caveats, weak reasoning, internal contradictions, source problems, and decision risk, then produces a corrected version when possible.**\n\n**Supported:** Text · Markdown · DOCX · PDF · Source files\n\n### → Paste or upload the AI-generated output to start.\n\n© Emanuele BDC · [LinkedIn](https://it.linkedin.com/in/emanuelebdc)\nQuesto prompt non elimina la necessità del giudizio umano. Fa qualcosa di diverso: rende il controllo più sistematico.
\nÈ particolarmente utile quando l’AI viene utilizzata per report, analisi, documenti professionali, ricerche o contenuti che devono essere pubblicati.
\nIl principio che mi ha guidato nella progettazione è semplice: generare velocemente è utile, verificare bene è indispensabile.
\nPer altri prompt, esperimenti e contenuti sull’intelligenza artificiale puoi seguirmi su LinkedIn.
", "image": "https://emaf205.com/media/posts/18/ChatGPT-Image-6-set-2026-22_05_41-3.png", "author": { "name": "Emanuele BDC" }, "tags": [ "PROMPT" ], "date_published": "2026-09-06T22:06:33+02:00", "date_modified": "2026-09-07T00:14:53+02:00" } ] }