How to Keep ChatGPT From Hallucinating

ChatGPT produces hallucinations—incorrect claims stated with confidence—because the model generates plausible-sounding text, not verified facts. You cannot eliminate this entirely, but you can reduce it significantly by grounding answers in source material you provide, asking the model to flag its own uncertainty, and checking any claim that matters before you rely on it. A tool like fenbs lets teams record verified facts and lessons from corrected mistakes, so the same error does not need to be caught twice.

The most important shift is in how you use the model. ChatGPT is reliable for reasoning about material you give it. It is less reliable for retrieving specific facts it learned during training, especially recent information, precise statistics, citations and details about specific people or organizations.

Understand when hallucinations are most likely

Hallucinations happen more often in certain types of requests:

• Specific numbers and statistics: the model generates plausible figures rather than retrieving verified ones.
• Citations and sources: it may produce convincing-looking references to papers, articles or cases that do not exist.
• Recent events: the model's knowledge has a cutoff date; details about recent developments are unreliable.
• Specific people and organizations: names, titles, dates and affiliations are frequently wrong in small details.
• Technical specifics: exact version numbers, regulatory codes, legal citations and clinical details are high-risk.

Tasks that involve reasoning about material you provide—summarizing a document, comparing two positions, explaining a concept in plain language—are more reliable because the model is working with text in front of it rather than retrieving from training.

Ground answers in source material you provide

The single most effective step is to paste the relevant source material into the prompt and ask the model to work only from that. A question like "Summarize the key arguments in this article" is more reliable than "Summarize the arguments about X" without any source, because the model's answer is constrained to what you gave it.

Add an explicit instruction: "Only use information from the text I have provided. If the answer is not in that text, say so." This prompt addition does not guarantee compliance, but it shifts the behavior in the right direction and makes violations easier to catch.

Ask the model to flag its uncertainty

ChatGPT does not always volunteer uncertainty, but it will flag it if you ask. Add this to prompts where accuracy matters:

• "Mark any claim you are uncertain about with [uncertain]."
• "If you cannot find this information in the text I provided, say 'not in source' rather than providing an answer."
• "For any statistic or date you include, note whether it comes from the text I pasted or from your training data."
• "At the end of your response, list any claims that should be verified before this is used."

These instructions shift the conversation toward a working model where you review flagged claims rather than spot-checking everything.

Check what matters before you rely on it

No prompt instruction makes ChatGPT error-free. Any claim that will be used in a professional context—legal advice, medical information, financial figures, technical specifications, citations—should be verified against a primary source before it is passed on.

Decide in advance which claims require checking and which do not. A draft email summarizing a meeting you attended does not need source verification. A memo that includes regulatory requirements does. Make the verification expectation explicit in your team's workflow, not implicit.

What to do when ChatGPT produces an error

When you catch an error, correct it directly: "The figure you stated is wrong. The correct figure is X according to [source]. Revise your response." The model will usually accept the correction in the same conversation.

The more important step is recording the correction so it is available in future sessions. A lesson note attached to the project says: "ChatGPT incorrectly stated the filing deadline as 90 days. The correct deadline under the relevant rule is 60 days. Check the rule text directly." That note helps the next person or the next AI session avoid the same error.

How to do this with fenbs

1. Create a free account. Start with a project where ChatGPT hallucinations have already caused problems.
2. Add verified facts as AI context notes on the project. The assistant reads these at the start of a session, before it drafts anything.
3. Add standing rules to Decisions and rules: "Always flag uncertain claims. Never state a statistic without noting its source." Connected assistants read these rules first.
4. When an error is corrected, add a Lesson learned: what the error was, the correct version, and where to find the authoritative source. Search lessons before similar work starts.
5. Connect your AI app via Integrations and ask it to read the project context, including lessons, before starting any research or drafting task.

For a comparison of how different AI tools handle source grounding and web search, see Copilot vs ChatGPT and Claude vs ChatGPT. Different models have different strengths in citation and sourcing; testing more than one on high-stakes research tasks is worthwhile.