There are three tiers of hallucination detection. Manual review is the slowest and least reliable. Automated scoring is faster but still after the fact. Real-time verification catches hallucinations before they happen. Here is every method, what it catches, and what it misses.
The most common approach in production today. A human reads the AI's output and checks it against known sources. This is also the worst approach.
What it misses: anything that looks right. Humans are inconsistent at spotting hallucinations. We tend to accept confident-sounding text at face value, especially when it is fluent and well-structured. The court record shows it: more than 2,000 decisions worldwide involve filings with AI-fabricated material that lawyers reviewed and submitted because it looked right.
Manual review also does not scale. If every AI output requires a human to fact-check it, you have not saved any time. You have just added a bottleneck. The time spent checking eats into the productivity gain the AI was supposed to deliver.
Use a second model or scoring system to evaluate the first model's output. Methods include:
Self-consistency checks. Ask the model the same question multiple times and compare answers. If the answers diverge, the model is uncertain. Catch rate is decent for factual claims but fails completely on consistent hallucinations - cases where the model gives the same wrong answer every time because its training data is wrong.
Entailment scoring. Use a natural language inference model to check if the output is supported by source documents. This works well for grounded summarisation tasks. It fails for open-ended generation where there is no source document to compare against.
LLM-as-judge. Use a second, larger model to evaluate the first model's output. GPT-4 evaluating GPT-3.5 outputs, for example. The problem: the judge model can hallucinate too. You are checking a guess with another guess. Agreement between two models is not the same as accuracy.
How well these work is contested. A 2026 audit of 22 detection methods found most perform near chance under controlled conditions, and that much of the reported progress came from how the benchmarks were built (PARALLAX, arXiv 2605.17028). But still reactive - these methods detect hallucinations after they have been generated. The user may have already seen or acted on the output.
Detecting a hallucination after it reaches the user is incident response. Preventing it from reaching the user is engineering.
Instead of detecting hallucinations after generation, prevent them by giving the model verified reality before it reasons.
Pre-flight context injection. Before the model generates any output, read the actual state of the environment and inject it into the context. If the AI is operating on a server, feed it the live state of that server. If it is answering a question about a system, give it the current, real-time data from that system.
Post-generation validation. After the model generates a response, validate every claim and every command against the live environment before it reaches the user. If the model says a file exists, check that it exists. If it proposes a command, verify the command is valid in the current state.
This is not detection. This is prevention. The hallucination never reaches the user because it is caught - or never generated in the first place - by the verification layer.
| Method | What it misses | Timing | Scales |
|---|---|---|---|
| Manual review | Anything that looks right | After delivery | No |
| Self-consistency | Mistakes the model repeats every time | After generation | Partially |
| Entailment scoring | Errors the source text cannot rule out | After generation | Yes |
| LLM-as-judge | Errors the judge model shares | After generation | Yes |
| Real-time verification | Prevention | Before delivery | Yes |
Every detection method shares the same assumption: the model will hallucinate and we need to catch it. This is damage control, not a solution.
The better question is not "how do we detect hallucinations?" but "how do we prevent the model from hallucinating in the first place?" And the answer is the same as it would be for a human: give it access to verified, up-to-date reality. Force it to check before it speaks.
Check operates at Tier 3. It checks every command and file write against the machine before it runs. A command that names something that does not exist is blocked before it runs, instead of detected after.
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