How do you verify AI output?

Why human review underperforms

A human in the loop is the standard control and it is weaker than it sounds, for a specific reason: language models produce output that is fluent, confident and structurally plausible. That is the hardest kind of output for a reviewer to catch errors in.

Add volume and the problem compounds. A reviewer checking the two hundredth extraction of the day is not reviewing; they are approving. This is not a discipline failure, it is how attention works.

Human review is necessary and it is not sufficient on its own for consequential work.

Why self-checking does not work

Asking a model to check its own answer feels like a free control and mostly is not. Same model, same training, same blind spots, same question — it will generally agree with itself.

You see some benefit from forcing a model to show reasoning or re-derive an answer, but it is not independent verification and should not be recorded as one.

Independent model verification

The mechanism is straightforward and the independence is the whole point:

1. A primary model does the work.
2. A different model, from a different family, receives the source and the claim — not the reasoning.
3. It is asked whether the claim is supported by the source.
4. Disagreements surface, specifically, with the source text.
5. Your rule decides: block, flag, or log and continue.

This catches a real class of error and misses others, notably anything both models get wrong in the same way. It is a control that reduces risk, not a guarantee, and that is exactly how it should be documented. CrossCheck AI is our implementation.

Where to apply which control

• Financial values. Independent verification, always. A wrong total is a real loss.
• Legal and clinical content. Independent verification plus qualified human review. Neither alone.
• Customer-facing text. Human review. Tone and judgement are the risk, not factual extraction.
• Internal drafts and summaries. Spot checks. Verifying everything here is a waste of money.
• Classification at volume. Sampled measurement against known answers, continuously, so drift is visible.

Measure it rather than assert it

Build a set of cases where you already know the correct answer, from your own real documents. Run them through the system monthly. Record the accuracy.

That gives you three things a framework cannot: a real number instead of a vendor claim, early warning when something drifts, and evidence for whoever eventually asks how you know it works.