All terms
Agents & tools
Model verification
Also known as: cross-model verification, AI review, verifier loop
Using a second AI model to check the first model's output — a structural defense against hallucinations and confident-wrong answers.
What it means
Model verification is the pattern of running output from one model through a second model whose job is to find problems. The verifier doesn't try to redo the work; it tries to break it. Common verifier prompts are: "where could this be wrong?", "what assumptions does this make?", "what's missing?", "spot the hallucinations."
This works because the second model has no investment in the first model's output. It's not defending its own work; it's just looking. Verification catches a different class of errors than self-review: confident-wrong claims, fabricated citations, edge cases the builder didn't think of, factual hallucinations.
It's not free. Verification adds latency (another LLM call), cost (more tokens), and complexity (you need to handle the verifier saying "this is fine" vs "this is wrong" vs "this is wrong here specifically"). It's worth it for high-stakes output: code that ships to production, research that informs decisions, drafts that go to clients. It's overkill for chat replies.
Example
Claude Code writes a function. You then ask GPT-5 to verify it: "Read this function and find at least 3 ways it could fail in production." GPT-5 spots an unhandled null case Claude missed. You fix it before merging.
Why it matters
AI hallucinations don't disappear with bigger models — they shift. Frontier models hallucinate less but more confidently, which means the failures that slip through are harder to spot by eye. Verification is the architectural answer: instead of trying to make any single model perfect, you build a check-step into the workflow.