How to Stop ChatGPT From Hallucinating: What Helps

You cannot make ChatGPT stop hallucinating completely. Reduce the risk by giving it reliable sources, asking it to separate evidence from guesses, and checking important claims before you use them. The practical goal is a repeatable review process, rather than a promise that the next answer will be true.

Tools like fenbs keep verified references and lessons from corrections with the project, so a new assistant session can start with evidence you already checked. That record helps your workflow; it does not make a model incapable of being wrong.

What should you do before asking a factual question?

1. State the exact question, intended audience, relevant place, and time period. Ask one research question at a time before requesting a polished report.
2. Identify the source that could settle the answer: a product manual, original paper, official policy, or the document you are analyzing.
3. Provide the relevant material or ask for a search of that source. Say which material the answer may rely on and what should remain unanswered.
4. Request a short answer with supporting passages or links for the claims that matter. Ask it to label inferences and missing evidence.
5. Check the answer against the actual sources before turning it into a client message, published article, or work instruction.

For example, a designer could ask whether a particular export format preserves a named feature in a specific application. Include the product version and official documentation. A broad request to list everything about exporting invites unrelated details that are harder to verify.

Choose the level of checking before you begin. Brainstorming fictional names needs different review from describing a real customer agreement. The AI governance hub covers the wider question of setting boundaries for AI work.

Why can a convincing answer still be wrong?

A hallucination is a plausible answer containing unsupported or false information. It can be a nonexistent citation, an invented quotation, or a real fact attached to the wrong person. Fluent writing is a presentation skill, so it should never be your evidence that a statement is accurate.

OpenAI explains that training and evaluation can reward guessing over admitting uncertainty↗ (opens in a new tab). A prompt can encourage restraint, but it cannot guarantee that the model will recognize every gap in its knowledge. Treat a confident response and a hesitant response as claims to check.

If an answer changes when challenged, do not settle the question by arguing until ChatGPT agrees with you. Find evidence outside the conversation. Agreement can make an exchange feel resolved while leaving the underlying factual question unanswered.

How do you make the evidence easier to inspect?

Ask for a small number of claims in a table with a source and a support note for each. The note should explain what the source establishes, rather than simply repeat the claim. Request an explicit gap when no source is available.

OpenAI's search documentation↗ (opens in a new tab) says search citations can be incomplete or incorrect. Open links yourself. An official-looking domain helps identify a source, but it does not establish that the sentence next to the citation accurately describes that page.

Keep the source, scope, and check date together in fenbs Links and listings, so the next session can inspect the same evidence instead of inventing a replacement reference.

Which instructions encourage useful uncertainty?

Give the assistant permission to stop. Say that an unanswered question is acceptable when the evidence is missing. Ask it to identify the missing information you could supply, such as a product edition or a date, rather than fill that gap with a likely answer.

Answer only the question below using the supplied sources. For each important factual claim, identify its supporting source and section. Separate direct evidence from inference. If the sources do not establish the answer, say what is missing. Do not invent a citation, quotation, date, or number.

Ask a focused follow-up: Which conclusion depends on an assumption? That is more useful than asking whether the entire answer is definitely correct. A claim of certainty from the same assistant is still another generated answer, rather than independent confirmation.

For coding tasks, require a check against the actual project and meaningful execution results. Our ChatGPT and Codex guide discusses that work context; a general factual prompting routine cannot replace running the relevant check.

What should you review before using the answer?

Review names, quotations, dates, numbers, and citations first. Then inspect the reasoning that joins those facts. Correct source material can still support an overbroad conclusion, such as treating one example as a universal rule.

For consequential work, have the appropriate person review the conclusion. A second model may offer another perspective, but agreement between models is not proof. The Claude and ChatGPT comparison can help you consider another assistant without treating a switch as an accuracy guarantee.

If you already have a misleading draft, follow the separate correction workflow. Here the priority is preventing an unchecked answer from becoming the starting point for later work.

How to do this with fenbs

1. Create a free account and add the project whose factual work you want to review.
2. Add the verified references to Links and listings. Include what each reference supports and when a person checked it.
3. Record a person's agreed review rules in Decisions and rules, such as requiring original sources for quotations and approval before publication.
4. Connect a supported assistant using Integrations. Ask it to read the project context before drafting and save unresolved checks in Where we left off.
5. After a correction, add a lesson describing the error, what established the correct answer, and what to check next time.

fenbs does not verify sources automatically or guarantee truthful answers. If you only need a one-time check, opening the source and correcting your document may be enough. It becomes useful when the same project, references, and review lessons need to survive several AI sessions.