How to Reduce AI Hallucinations with Prompts

What is an AI hallucination?

An AI hallucination is information that sounds plausible but is invented, inaccurate or unsupported by the available material. Common examples include fake citations, confident wrong dates, blended events and conclusions drawn from missing context.

The risk is not strange wording. It is professional-sounding wording that readers mistake for evidence.

Why “do not make mistakes” is not enough

A model still needs to know what counts as accurate, which sources it may use, what to do when information is missing, whether evidence is required and whether it is allowed to say “I cannot confirm that.” Turn accuracy into explicit actions.

Five prompt methods that reduce hallucination risk

1. Limit the information source

For a document, report or dataset task, tell the model to use only the supplied source and label missing facts.

Answer only from the material below.

[SOURCE]
{{paste material}}
[/SOURCE]

Rules:
- Do not add facts outside the source.
- Write “not mentioned in the source” when needed.
- Do not infer from general knowledge.
- Every conclusion must be traceable.

2. Separate facts, inferences and recommendations

Ask for three sections: confirmed facts, reasonable inferences with their basis, and recommendations or assumptions. Mark anything unclear as unconfirmed.

3. Require evidence and citations

For numbers, dates, people, laws and research claims, require a source. If no reliable source is available, write “no verifiable source available.” Never invent a link to make the answer look complete.

4. Allow explicit refusal

When the information is insufficient for a reliable conclusion, do not guess. Answer: “There is not enough information to confirm this.”
Then list known information, missing information and what needs verification.

5. Make verification a workflow step

1. Extract key facts and entities.
2. List the evidence required.
3. Check whether the source contains it.
4. Mark uncertainty.
5. Write only from confirmed information.

A reusable reliable-answer prompt

You are a research assistant who prioritizes factual accuracy.

Complete this task using only the supplied material: {{task}}

Rules:
1. Use only explicitly supported information.
2. Separate confirmed facts, reasonable inferences and recommendations.
3. Do not add unsupported details.
4. Cite the basis for each key conclusion.
5. Show conflicting source claims separately.
6. Say “cannot confirm” when evidence is insufficient.
7. Do not invent citations, data, names, dates, links or papers.

Output: conclusion / confirmed facts / inferences / cannot confirm / next checks

Use Clipcat to turn the rules into a workflow

Reliable output comes from a repeatable process, not a magic sentence. Start with a nearby template in the Clipcat free prompt library, then add your source boundary, evidence standard, refusal rule and verification checklist.

For reference videos, use Video Analysis to organize hooks and claims, or Video to Prompt to turn visual direction into an editable brief. These tools help structure material; important facts still need to be checked against the original source.

Human checks still matter

• Open original sources to verify numbers, dates, laws, contracts, citations and links.
• Provide primary documents, time ranges, units, definitions and exceptions.
• Do not confuse fluent writing with accurate information.

Conclusion

Reducing hallucination risk is a prompt-and-process problem. Define the source, separate fact from inference, require evidence, allow refusal and verify before publishing or deciding. Use the Clipcat prompt library as a starting point, then adapt each template to your own material and standards.