2. Force Abstention

Add a rule to every factual prompt: "If you cannot verify this from the provided information, say 'I'm uncertain' or 'Not specified.'" Forcing the model to abstain when evidence is missing is a widely recommended strategy . A "reality filter" custom instruction — prohibiting the presentation of inferred or speculative content as fact — can be set in your ChatGPT personalization settings so it applies to every conversation .

3. Require Citations and Traceability

Ask ChatGPT to cite specific sources for each claim, including the URL or reference document . Note that citations themselves can be hallucinated, so treat them as claims to verify rather than proof . Nevertheless, requiring traceability pushes answers toward a more structured, source-grounded format .

4. Break Complex Questions Into Small, Direct Ones

Instead of one broad question, ask a series of narrow sub-questions . Narrowing the task reduces the model's degrees of freedom, making it less likely to generate unsupported content . For example, replace "What are the effects of climate change?" with three specific questions about temperature, sea level, and agriculture.

5. Use Persona and Explicit Constraints

Define a clear role and ban vague terms . Example: "You are a research assistant. Only use information from the document I provided. If the answer is not in the document, say 'Not specified.'" Strict behavioral guardrails keep the model grounded in provided context .

6. Require Multi-Step Verification

Add a meta-prompt: "Before replying, verify each factual claim against the provided sources and flag anything you cannot verify." This prompts the model to self-check before outputting text . Asking the model to show its reasoning or sources lets you inspect its claims, but should not replace your own verification .

7. Always Verify Critical Claims Yourself

Treat every specific factual output — dates, statistics, names, laws, medical or financial details — as a hypothesis to be checked, not a conclusion to trust . Use independent sources to confirm high-stakes claims before acting on them. Re-ask the same question in a slightly different way at a different time; inconsistent answers are a strong signal of hallucination .

What Does Not Work Reliably

• Simply asking "Are you sure?" is weaker than asking for sources, evidence, abstention, or explicit verification steps .
• Relying on citations without checking them is risky — citations themselves should be treated as claims to verify .

The Key Caveat

Hallucinations cannot be fully stopped; the goal is damage reduction, not elimination . Combining multiple techniques — grounding in documents, abstention prompts, citation requirements, and manual verification — gives you a much stronger defense than relying on any single method .

2. Force Abstention

Add a rule to every factual prompt: "If you cannot verify this from the provided information, say 'I'm uncertain' or 'Not specified.'" Forcing the model to abstain when evidence is missing is a widely recommended strategy . A "reality filter" custom instruction — prohibiting the presentation of inferred or speculative content as fact — can be set in your ChatGPT personalization settings so it applies to every conversation .

3. Require Citations and Traceability

Ask ChatGPT to cite specific sources for each claim, including the URL or reference document . Note that citations themselves can be hallucinated, so treat them as claims to verify rather than proof . Nevertheless, requiring traceability pushes answers toward a more structured, source-grounded format .

4. Break Complex Questions Into Small, Direct Ones

Instead of one broad question, ask a series of narrow sub-questions . Narrowing the task reduces the model's degrees of freedom, making it less likely to generate unsupported content . For example, replace "What are the effects of climate change?" with three specific questions about temperature, sea level, and agriculture.

5. Use Persona and Explicit Constraints

Define a clear role and ban vague terms . Example: "You are a research assistant. Only use information from the document I provided. If the answer is not in the document, say 'Not specified.'" Strict behavioral guardrails keep the model grounded in provided context .

6. Require Multi-Step Verification

Add a meta-prompt: "Before replying, verify each factual claim against the provided sources and flag anything you cannot verify." This prompts the model to self-check before outputting text . Asking the model to show its reasoning or sources lets you inspect its claims, but should not replace your own verification .

7. Always Verify Critical Claims Yourself

Treat every specific factual output — dates, statistics, names, laws, medical or financial details — as a hypothesis to be checked, not a conclusion to trust . Use independent sources to confirm high-stakes claims before acting on them. Re-ask the same question in a slightly different way at a different time; inconsistent answers are a strong signal of hallucination .

What Does Not Work Reliably

• Simply asking "Are you sure?" is weaker than asking for sources, evidence, abstention, or explicit verification steps .
• Relying on citations without checking them is risky — citations themselves should be treated as claims to verify .

The Key Caveat

Hallucinations cannot be fully stopped; the goal is damage reduction, not elimination . Combining multiple techniques — grounding in documents, abstention prompts, citation requirements, and manual verification — gives you a much stronger defense than relying on any single method .