Every practical hallucination detector reduces to the same question: can each claim in this answer be traced back to evidence? The implementations differ — natural language inference models, claim decomposition plus retrieval, LLM-as-a-judge with a faithfulness rubric, token-level uncertainty — but they all compare generated text against a reference. When a claim has no support, it's flagged. This is grounding verification, and it is the correct first layer for any production RAG system.
The blind spot appears the moment your knowledge base disagrees with itself. If your refund policy says 14 days in one Confluence page and 30 days in a SharePoint PDF, a grounding-based detector sees a claim with clear supporting evidence and passes it. The answer is faithful. It is also wrong half the time, depending on which chunk was retrieved. No amount of answer-level detection fixes that, because the error was never in the generation step.
The rule of thumb: grounding detection tells you whether the model invented something. Conflict detection tells you whether your knowledge can be trusted at all. You need both, and the second one has to run on the corpus, not on the answer.
Building detection that holds in production
1. Decompose answers into atomic claims
Whole-answer scoring is too coarse — a five-sentence response can be four-fifths correct and still mislead. Split each output into individual verifiable claims, then check each one against the retrieved context separately. Claim-level detection gives you precise flags you can act on, and it makes false positives easy to audit.
2. Score grounding with an explicit rubric and a required citation
Use a judge model or an NLI model to label each claim supported, contradicted, or unsupported — and require the exact passage that justifies the label. Forcing a citation collapses most of the judge's own hallucination rate and turns the detector's output into something a reviewer can verify in seconds.
3. Run conflict detection across the corpus, not just the answer
Compare passages that answer the same question against each other and against designated authoritative sources. Contradictions, stale duplicates, and partial overlaps surface here — before a user ever asks the question. This is the layer that turns detection from reactive triage into prevention.
Detection is only useful if it points at a document
Flagging an answer tells you a request failed. It doesn't tell you which of your ten thousand documents caused it, or stop the next hundred users from hitting the same contradiction. Alignode is the reliability layer that closes that gap.
- Continuously scan Confluence, Notion, SharePoint, Slack, and Drive for contradictory, stale, and duplicated statements.
- Trace every detected conflict back to the specific documents and owners responsible for it.
- Reconcile conflicting passages against a validated source of truth so retrieval stops returning both.
Frequently asked questions
What is a hallucination detector?
A hallucination detector is a system that checks whether the claims in a model's output are supported by evidence. Typical implementations decompose the answer into atomic claims and verify each against retrieved context using a natural language inference model or an LLM-as-a-judge with a faithfulness rubric, labeling claims supported, contradicted, or unsupported.
How do you detect hallucinations in a RAG system?
Compare each claim in the generated answer against the passages that were actually retrieved, and require a citation for every supported label. Then, separately, check the retrieved passages against each other: if two sources disagree, the answer can be faithful to its context and still wrong. Both checks are needed for reliable RAG.
Can a hallucination detector catch outdated information?
Not reliably. Grounding-based detection only asks whether a claim is supported by the retrieved text. If the retrieved document is out of date, the claim looks perfectly supported. Catching stale answers requires evaluating the knowledge base itself — freshness, contradiction against newer sources, and ownership — rather than the answer alone.
Is LLM-as-a-judge good enough for hallucination detection?
It is strong for faithfulness scoring when the rubric is explicit and you force the judge to cite the supporting passage. It is not sufficient on its own, because the judge only sees the question, the retrieved context, and the answer — never the rest of the corpus — so it cannot know that another document contradicts the one it is reading.
What accuracy should we expect from detection?
It depends far more on your corpus than on the detector. In clean, consistent knowledge bases claim-level grounding checks are highly reliable. In corpora with duplicated and contradictory documentation, precision collapses — not because detection is weak, but because there is no single correct answer to detect against. Cleaning the knowledge layer raises the ceiling for every downstream metric.
Related guides
Detect the conflict before the user finds it.
See how Alignode scans your knowledge base for contradictions and keeps your AI grounded in documented truth.