The Tool Desk
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What reduces hallucinations—and what does not?
Hallucinations are factual claims that are wrong or unsupported. A polished tone, high confidence, or a citation beside a claim is not proof. Useful controls work together: clarify what the model should do, give it appropriate evidence, make uncertainty visible, and evaluate the result against a defined standard.
- Prompts can narrow the task and make the answer easier to assess, but cannot guarantee truth.
- Grounding can supply current or specialist evidence, but irrelevant, stale, or incorrect retrieved material can mislead the model.
- Citations and excerpts make claims easier to audit, but only if the cited material actually supports them.
- Abstention is useful when evidence is missing; a system that refuses answerable questions too often is not useful.
- Testing and human review help reveal where a workflow fails. Important decisions still require checking consequential facts against original sources.
Provider-reported model comparisons are not evidence that one model or prompting method will perform best on your task. OpenAI’s 2025 GPT-5 system card reports model-specific results under its own evaluation conditions: GPT-5 main had a 26% smaller hallucination rate than GPT-4o, and GPT-5 thinking had a 65% smaller rate than o3. OpenAI defines the claim-level rate as the percentage of factual claims with minor or major errors. The system card also reports that human reviewers agreed with its factuality grader in 75% of the described validation assessments. These figures are not estimates of the effect of user practices, a universal comparison across providers, or a forecast for your use case. OpenAI’s GPT-5 System Card
How can I make ChatGPT or another AI more accurate?
Make the task specific enough to judge, set relevant boundaries, and provide or retrieve evidence for factual answers. Use this workflow for research, writing, analysis, or decision support:
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- Name the job and audience. For example: “Summarize the attached report for a nontechnical reader” is easier to evaluate than “Tell me about this topic.”
- Set boundaries. Specify the time period, jurisdiction, source set, or format when they matter. If the answer must rely only on supplied documents, say so explicitly.
- Supply suitable evidence. Attach trusted documents or use a reliable search or retrieval feature for current facts. Do not assume the model’s training data includes the latest information.
- State what to do when evidence is missing. Ask the model to identify gaps, flag unsupported premises, distinguish sourced facts from inference, and say when it cannot answer from the available material.
- Ask for traceable support. For each important factual claim, request a citation or the exact supporting passage. Then check that the source is relevant and that the passage entails the claim.
- Verify important claims yourself. Use the original source, not the model’s confidence or its own follow-up self-check, as the final check for consequential facts.
For document-based work, Anthropic’s Claude documentation recommends extracting exact quotes, basing analysis on those quotes, citing evidence for claims, and retracting claims when no supporting quote can be found. Restricting outside knowledge can help when the task must use supplied documents. These methods reduce risk but do not eliminate hallucinations. Anthropic’s guidance on reducing hallucinations
How can I get an AI to cite sources—and tell whether they support the answer?
Ask for evidence at the claim level, not just a bibliography at the end. For each material claim, look for a source that is authoritative for the subject, relevant to the claim, and specific enough to support it. Open the source and compare its wording with the answer: a citation may be real yet irrelevant, incomplete, or weaker than the claim it accompanies.
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- Check whether the linked page or quoted passage says what the model claims.
- Check dates, definitions, jurisdiction, and other qualifications that may narrow what the source establishes.
- Separate a directly supported fact from an inference or recommendation.
- Remove, qualify, or correct claims that the evidence does not support.
For a system that must answer only from a document set, explicitly limit its evidence to that set and require it to point to the relevant passages. For questions needing current information, use a search-grounded workflow and inspect the retrieved sources rather than treating the presence of search results as validation.
How should developers reduce hallucinations in an AI application?
Evaluate the complete workflow: source retrieval, the model’s use of retrieved material, and the final response. OpenAI’s accuracy guide treats prompt design, retrieval-augmented generation (RAG), fine-tuning, and evaluation as different optimization levers. It warns that RAG can fail when retrieval returns incorrect material or too much irrelevant context, and that a model can still mishandle correct context. OpenAI’s guide to optimizing LLM accuracy
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Build a representative test set
Before changing the prompt or model, collect examples representative of the application and define what counts as correct for that use—not merely fluent or well-formatted. Include questions with sufficient evidence, missing-evidence cases, and unsupported premises. Keep some examples aside as a hold-out set if you fine-tune, so you can check that changes generalize rather than only fit the examples used for training.
Diagnose the failure before choosing a fix
- The needed source was not retrieved: improve the search or retrieval process, or make the relevant source available.
- The wrong source was retrieved: adjust retrieval toward authoritative, relevant material.
- Too much noisy context was returned: reduce irrelevant material and test whether the model receives enough useful context without being overwhelmed.
- The model misread valid context: improve instructions, examples, or the way evidence is presented, then test whether claims follow the source.
Test retrieval quality separately from the model’s use of retrieved evidence; success at one stage does not guarantee success at the other. RAG libraries such as LangChain and LlamaIndex are examples mentioned in OpenAI’s guide, not guarantees of accuracy.
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Choose changes based on the failure pattern
If the task behavior is inconsistent, clearer instructions, examples, or fine-tuning may help. If answers lack facts because the source is missing or outdated, improve retrieval or provide more relevant context; fine-tuning is not a substitute for updating factual knowledge. After changing the prompt, model, retrieval pipeline, or source collection, run the evaluation set again.
Add review and monitoring suited to the risk
Use a post-generation claim check or human review when factual reliability matters, and set the review intensity according to the possible harm of an error. Google recommends application-specific testing, feedback, monitoring, and iteration; its Gemini API guidance says that outputs still require post-processing and rigorous manual evaluation. Google also recommends Search grounding to reduce potential factual inaccuracies, but grounding is a feature whose availability depends on the product and workflow. Google’s Gemini API safety and factuality guidance
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How do I compare controls or frontier models?
Compare alternatives on the same representative task set, with the same source material and success criteria. A useful evaluation considers:
- Evidence freshness: Does the task depend on changing facts, and can the workflow retrieve current sources?
- Source quality and relevance: Does retrieval return pertinent, authoritative material without excessive noise?
- Traceability: Can important claims be checked against citations or passages?
- Abstention: Does the system acknowledge missing evidence without refusing answerable questions too often?
- Task-specific factual accuracy: How does it perform on examples that represent the actual application?
- Cost and latency: Measure these in the intended deployment; the provider guidance cited here does not establish a universal comparison.
- Consequence of error: Set acceptable thresholds and human-review requirements for the real-world risk.
Do not treat repeated samples or agreement between model responses as independent verification. Different outputs can be a warning that an answer is unstable, but agreement alone does not establish that it is correct. Likewise, a lower apparent error rate is not useful if the system achieves it by refusing nearly everything.
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