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To reduce AI hallucinations, use web search or deep research for questions that depend on current information, provide reliable reference material for specialized tasks, and ask the model to show its assumptions and evidence. Then check important claims against the original sources. These steps make errors easier to catch; no setting guarantees a correct answer. For consequential medical, legal, or financial decisions, keep a qualified human professional involved.
What is an AI hallucination, and how can you recognize one?
An AI hallucination is an answer that presents unsupported or incorrect information as though it were reliable. It can be a wrong date or fact, a fabricated quotation or citation, or a confident response to a question whose wording is ambiguous. Errors can also happen when a model misunderstands a source, combines details that belong to different contexts, misquotes a passage, or fills a gap with plausible-sounding text.
Fluent writing is not evidence of accuracy. A response can sound precise and still misstate what a source says, cite a source that does not support its claim, or omit a qualification that changes the meaning. OpenAI’s Help Center cautions that ChatGPT is not always right and recommends treating it as a first draft rather than a final source. The practical consequence is simple: use settings and prompts to improve the conditions for a good answer, but use verification to decide whether to trust it.
Which settings help with current or changing questions?
Turn on web search or deep research when an answer depends on recent, niche, or changing information. These tools can retrieve current material and provide citations, but the citations themselves can still be incomplete, outdated, or incorrect.
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Use search for focused, timely questions
Search is useful when you need a current fact, a recent policy, or information that may have changed since a model learned it. Ask the question with enough context to identify the relevant country, date, product version, or organization. If the answer includes sources, open them rather than relying on the summary alone.
Use deep research when the question needs synthesis
For a topic that requires gathering and comparing information across multiple sources, a deep-research mode can help organize that work and show citations. It does not remove the need to inspect the evidence: a cited page may not support the exact sentence, and a useful-looking collection of sources may still omit an important perspective.
Check whether the feature is available in your version
Tool names and availability can differ by plan, platform, workspace, and date. Look for the search or research control in the interface you actually use; do not assume that another user’s menu or instructions match yours. If a tool is unavailable, provide dated source material yourself and treat the answer as limited to that material and the model’s existing knowledge.
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Ask for a response that separates conclusions from assumptions and uncertainty, and requests a source for each material factual claim. This format improves auditability, not truthfulness: a model can still invent evidence, misunderstand a source, or give an unsupported answer in a neatly organized format.
State the task and the boundaries
Specify what decision or question you need answered, the relevant time period and location, and what sources count as authoritative. For example, distinguish a current rule from a historical one, or ask for information about a particular edition rather than a product in general. If there are facts the model must not assume, identify them explicitly.
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Request evidence and uncertainty in separate parts
A useful instruction is: “Answer the question, list any assumptions, identify what is uncertain, and give a source for each important factual claim. If the sources do not establish an answer, say so rather than guessing.” This makes it easier to find which statements need checking. It does not force the model to follow the instruction accurately, so treat unsupported confidence as a reason to investigate, not as proof.
Define what a complete answer must contain
For complex work, tell the model what a correct response needs to address. A comparison might need to cover dates, eligibility, exceptions, and the source for each; a technical explanation might need to distinguish documented behavior from inference. Clear acceptance criteria expose omissions and reduce the temptation to mistake a polished response for a complete one.
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Supply authoritative reference text when the task depends on specialized or organization-specific information. Retrieval-augmented generation (RAG) works by retrieving relevant content before generating a response, which can ground an answer in material the model would not reliably recall on its own.
Use the relevant source itself where possible: a policy, manual, contract, official guidance, or other primary material. Ask the model to distinguish what the source states from its interpretation, and to identify the passage that supports each important conclusion. If the source does not address a point, ask it to mark that point as unstated rather than infer a rule.
Reference material is not automatically trustworthy or complete. OpenAI’s accuracy guidance warns that retrieval can introduce hallucinations when the retrieved material is wrong or noisy. Check that the document is authoritative, relevant to the question, and current for the applicable version or date. If you provide several sources, tell the model how to handle conflicts; do not assume that it will identify an outdated document or resolve a disagreement correctly.
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How do I verify an important answer step by step?
Verify the claims that could change your decision, not just whether the answer looks well sourced. For each important claim, check the underlying passage, the publisher, the date, and whether the wording in the answer goes beyond what the source establishes.
- Break the answer into claims. Separate dates, numbers, quotations, recommendations, and cause-and-effect statements. A paragraph may contain several claims with different evidence needs.
- Open every cited source that matters. Do not rely on a citation label, a search snippet, or the model’s description of a page. Read the relevant section in context.
- Compare the exact wording. Check whether the source supports the claim as phrased, including qualifiers, exceptions, geography, version, and date. A source that mentions a subject is not necessarily evidence for the answer’s conclusion.
- Check who published it and when. Prefer an authoritative source for the specific question, and confirm that its information applies to the period or edition you care about. An old page can remain online after the underlying information changes.
- Cross-check high-stakes claims. Compare independent authoritative sources when the consequences of an error are serious. Multiple pages repeating the same unsupported statement are not necessarily independent confirmation.
- Revise or stop when support is missing. Ask the model to correct a claim using the source you found, or leave the point unresolved. Do not turn a plausible answer into a verified one by repeating the question until you get the desired response.
OpenAI’s family guidance notes that models can misunderstand source material, mix details, misquote, or fill gaps with plausible text. That is why checking a citation means checking the passage—not merely confirming that the linked page exists.
Should I turn on memory to improve accuracy?
Memory is a personalization control, not a truth control. It can help an assistant retain useful context about your preferences, but a remembered detail does not verify an external fact or make a response more current.
Review saved memories and reference-chat-history controls when past context is unwanted or could bias a new answer. Correct or delete memories that are inaccurate or no longer appropriate. Disabling reference to chat history does not delete the original chats, so manage the history itself separately if your aim is to remove those conversations.
For an answer where prior context could distort the result, state the relevant facts in the current prompt and ask the model to use only those facts and the sources you supply. Feature labels and controls may vary across plans, platforms, and workspaces, so use the settings available in your own interface rather than assuming a universal menu path.
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Do newer or reasoning-focused models eliminate hallucinations?
No. Model evaluations can show relative performance on a defined benchmark, but they do not guarantee an accurate response to an individual prompt. OpenAI’s 2025 Deployment Safety Hub reports that, in its stated evaluation, GPT-5-main had a 26% smaller hallucination rate than GPT-4o, while GPT-5-thinking had a 65% smaller rate than OpenAI o3. The same evaluation reports 44% fewer responses with at least one major factual error for GPT-5-main than GPT-4o, and 78% fewer for GPT-5-thinking than o3.
Those are results under OpenAI’s benchmark and grading procedure, tied to the evaluated systems and methods. They are not a prediction that a particular answer will be correct, nor a substitute for checking an answer that matters. Performance can vary with the task, prompt, sources, and model version.
What should I do when the answer could affect health, money, or legal rights?
Keep a qualified human professional in the loop. OpenAI Academy advises users to double-check critical facts with trusted sources and to keep a human involved in important work; for legal, medical, and financial decisions, consult a professional before acting.
Use an AI response to organize questions, summarize material you provide, or identify issues to discuss—not as the final authority on a decision with serious consequences. Bring the underlying sources and relevant details to the professional, and ask them to confirm the interpretation for your circumstances. If the model’s answer conflicts with a qualified professional or an authoritative source, do not resolve the conflict by trusting the more confident wording.
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How do the main accuracy controls compare?
Choose a control according to the kind of failure you are trying to reduce. Freshness, grounding, inspectability, context control, and human review solve different problems; none is a universal accuracy switch.
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| Control | What it helps with | What it does not establish | Best check |
|---|---|---|---|
| Web search or deep research | Retrieving current information and surfacing citations for claims that may have changed. | That citations are complete, current, or correctly interpreted. | Open the cited source and check the supporting passage and date. |
| Attached authoritative references | Grounding specialized answers in material relevant to the task. | That the retrieved document is correct, current, or sufficient. | Confirm the source’s authority and ask whether each conclusion is stated or inferred. |
| Evidence-first prompting | Making assumptions, uncertainty, and claims easier to inspect. | That the model will obey the prompt or provide genuine support. | Verify the evidence independently rather than trusting the requested format. |
| Memory and history controls | Managing which personal context may influence a response. | That remembered context is factual or that the answer is accurate. | Review, correct, or remove context that is irrelevant or wrong. |
| Human professional review | Escalating decisions where a mistake could have serious consequences. | A guarantee that every AI statement was checked before consultation. | Bring the actual sources and circumstances to a qualified professional. |
What are the most reliable habits to keep?
Accuracy comes from a workflow, not one setting. Match the tool to the question, give the model good evidence, make the answer inspectable, and verify consequential claims at their source.
- Use search for information that may have changed; inspect the citations instead of accepting the summary.
- Provide authoritative reference material for specialized tasks, while checking that the material itself is relevant and reliable.
- Split complicated requests into smaller questions with clear criteria for a satisfactory answer.
- Ask for assumptions and uncertainty, and treat those as prompts for review rather than guarantees of honesty.
- Correct or remove unwanted personal context; memory does not validate factual claims.
- In high-stakes situations, consult a qualified human before acting.
Frequently Asked Questions
Can I make ChatGPT say “I don’t know” instead of guessing?
You can explicitly instruct it to say when the available sources do not establish an answer, but that instruction is not foolproof. Check whether the response actually distinguishes documented facts from inference, especially when it sounds unusually certain.
Are AI citations always real and relevant?
No. A citation can be missing, outdated, or unrelated to the precise claim it accompanies. The useful test is whether the source’s own text supports the answer in its stated context.
Is Google Search grounding in Gemini a guarantee of accuracy?
No. Google AI for Developers describes grounding with Google Search as a way to provide more accurate answers and cite verifiable information beyond the model’s knowledge cutoff, not as a promise that every response is correct. The cited sources still need review.
Does deleting a saved memory delete the conversation it came from?
Not necessarily. Managing a saved memory and deleting the original chat are separate actions; disabling chat-history reference also does not delete the chats themselves.
Quick Recap
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