How do I evaluate whether an LLM answer is actually correct?

To evaluate whether an LLM answer is actually correct, use a mix of verification methods rather than trusting the surface quality of the response.

1) Check for the type of task

Different tasks need different validation:

• Factual questions: verify against reliable sources.
• Math/code: test the result directly.
• Reasoning/analysis: check whether the steps are valid and conclusions follow.
• Creative writing: “correctness” may mean consistency, not factuality.

2) Look for specific, checkable claims

Break the answer into atomic claims:

• dates
• definitions
• numbers
• named entities
• cause/effect statements
• quoted text

Then verify each claim independently. An answer can be mostly right but still contain one important falsehood.

3) Cross-check with authoritative sources

Use sources appropriate to the domain:

• General facts: reputable reference sites, textbooks, official documentation
• Medical/legal/financial: primary or professional sources, not just web summaries
• Programming: official docs, language specs, source code, tests

Prefer sources that are:

• current
• primary
• domain-specific
• consistent with one another

4) Test the answer in practice

For tasks where execution is possible:

• Code: run it
• Math: recompute independently
• Data analysis: reproduce with the same dataset
• Instructions: follow the steps and see if they work

If the answer predicts an outcome, check that the outcome actually occurs.

5) Inspect the reasoning, not just the conclusion

Even a correct-looking answer may be wrong if the logic is weak.

Ask:

• Are assumptions stated?
• Do the steps follow logically?
• Are there hidden leaps?
• Does it confuse correlation with causation?
• Does it overgeneralize from one example?

A solid answer should be traceable from premises to conclusion.

6) Watch for hallucination signals

Common warning signs:

• overly confident tone with no caveats
• specific-looking citations that may be invented
• unusual named entities, numbers, or dates
• answers that sound plausible but are hard to verify
• contradictions within the response

If it gives sources, verify that the sources actually exist and support the claim.

7) Ask the model to justify or self-check

Useful prompts:

• “Show the steps.”
• “What assumptions are you making?”
• “List the sources for each claim.”
• “What could make this answer wrong?”
• “Provide a concise verification checklist.”

This doesn’t prove correctness, but it can expose weak reasoning.

8) Use redundancy

Ask:

• the same question in a different way
• another model
• a human expert
• multiple sources

Agreement isn’t proof, but disagreement is a strong sign to investigate further.

9) Evaluate confidence separately from correctness

An LLM can sound confident while being wrong. Treat confidence as a clue, not evidence.

A better question is:

• “What evidence supports this?”
• “How easy would it be to verify?”
• “What is the failure mode if it’s wrong?”

10) For high-stakes domains, don’t rely on the LLM alone

For medical, legal, safety, or financial decisions:

• verify with qualified professionals
• consult primary sources
• use the model only as a drafting or brainstorming aid

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A practical quick checklist

Before trusting an answer, ask:

1. What exact claims is it making?
2. Which claims can I verify?
3. Do reliable sources support them?
4. Does the logic actually work?
5. Can I test it directly?
6. Are there signs of hallucination?
7. What happens if it’s wrong?

If you want, I can also give you:

• a short rubric for grading LLM answers,
• a workflow for fact-checking, or
• a checklist for coding/math answers specifically.