Hallucination detection identifies when AI generates false or unsupported claims that sound confident but have no basis in source data. It works by comparing AI outputs against verified sources, checking for logical consistency, and flagging claims that cannot be traced to evidence. For businesses, this prevents AI from confidently spreading misinformation to customers and teams.
Your AI answers a customer question with complete confidence. The answer is wrong.
The customer trusts it because it sounds authoritative. They act on it. Now you have a problem.
The AI did not lie. It just invented something plausible and presented it as fact.
AI can be confidently wrong. Detection catches it before customers do.
QUALITY & RELIABILITY LAYER - Building trust by catching falsehoods.
Hallucination detection identifies when AI generates claims that have no basis in provided context or verified sources. The AI sounds confident, uses proper grammar, and constructs logical-seeming sentences. But the content is fabricated.
Detection works by comparing what the AI says against what it should know. If a claim cannot be traced back to source documents, retrieved context, or verified facts, it gets flagged. The AI might be right by coincidence, but it did not have a valid basis for the claim.
AI models do not know what they do not know. They fill gaps with plausible patterns. Detection catches the gaps before they become customer-facing problems.
Hallucination detection solves a universal problem: how do you trust information from a source that cannot distinguish between what it knows and what it is guessing? The same pattern appears anywhere confident claims need verification.
Take a claim. Trace it back to sources. If the source says it, the claim is grounded. If the source does not say it, flag it. If there is no source, treat it as suspect.
Toggle detection on or off, then send AI responses to customers. Watch what happens when wrong answers slip through versus get caught.
AI responses go directly to customers
How long do I have to return this product?
Return window: 30 days from purchase date
You have 60 days from your purchase date to return the product for a full refund.
Detection is disabled. The AI response will go directly to the customer.
Three ways to catch AI making things up
Check claims against documents
For every factual claim in the AI output, find the supporting passage in the source documents. If the AI says "our refund policy is 30 days," there should be text in the policy document that says 30 days. No source, no trust.
Ask the same question different ways
Rephrase the question and ask again. A grounded answer stays consistent. A hallucinated answer often changes. "What is the return window?" and "How long do customers have to return items?" should produce matching numbers.
Flag when the model hedges
Monitor token-level probabilities. When the model is uncertain, it produces lower-confidence token sequences. Answers where confidence drops below threshold get flagged for review even if the content sounds definitive.
Answer a few questions to get a recommendation tailored to your situation.
Do you have access to the source documents the AI should reference?
A customer asks the AI about return windows. The AI confidently states "60 days" when the actual policy is 30 days. Without hallucination detection, the wrong answer reaches the customer. With detection, the fabricated claim is caught and either corrected or escalated to a human before damage is done.
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This component works the same way across every business. Explore how it applies to different situations.
Notice how the core pattern remains consistent while the specific details change
You read the AI output and it seems reasonable. It uses proper terminology, follows logical structure, and matches your expectations. So you trust it. But plausibility is not accuracy. Hallucinations sound right by definition.
Instead: Verify against sources, not intuition. If you cannot trace a claim to a document, treat it as unverified.
The AI includes a citation: "See refund policy section 4.2." You assume this means it read section 4.2. But AI can fabricate citations. It can reference documents that do not exist or quote passages that say something different.
Instead: Validate citations programmatically. Check that the cited source exists and contains the claimed information.
To reduce false positives, you set generous thresholds. Now most outputs pass review. But you have traded false alarms for missed hallucinations. Customers receive confidently wrong answers and your trust is eroding.
Instead: Start strict and loosen based on observed error rates. False positives are better than false negatives for customer-facing content.
An AI hallucination occurs when a language model generates information that sounds plausible and confident but is factually incorrect, fabricated, or unsupported by its training data or provided context. Unlike human lies, the AI is not intentionally deceiving. It is pattern-matching and sometimes those patterns produce false outputs. Hallucinations range from minor inaccuracies to completely invented facts, citations, or events.
AI models hallucinate because they predict the most likely next tokens based on patterns, not truth. When asked about topics with limited training data, they fill gaps with plausible-sounding content. Overconfident prompting, insufficient context, and questions beyond the model knowledge cutoff all increase hallucination risk. The model cannot distinguish between what it knows and what it is inventing.
Detection methods include: source verification (checking claims against provided documents), consistency checking (asking the same question multiple ways), confidence thresholds (flagging outputs where the model shows uncertainty), citation validation (verifying that referenced sources exist and say what the AI claims), and cross-model verification (comparing outputs from multiple models). Production systems typically combine several approaches.
Any industry where AI provides factual information to customers or informs decisions needs detection. Healthcare, legal, and financial services face regulatory consequences for misinformation. Customer support teams risk reputation damage when AI gives wrong answers. Internal knowledge systems can spread false information across organizations. The higher the stakes of incorrect information, the more critical detection becomes.
No, current AI architectures cannot guarantee zero hallucinations. You can reduce them through retrieval-augmented generation (grounding in source documents), lower temperature settings, explicit instructions to acknowledge uncertainty, and domain-specific fine-tuning. But the most reliable strategy is detection and filtering rather than prevention. Assume hallucinations will occur and build systems to catch them.
Fact-checking verifies claims against external knowledge bases and trusted sources. Hallucination detection specifically catches claims the AI invented, whether or not they happen to be true. A hallucination might coincidentally be accurate, but it was still fabricated. Detection focuses on whether the AI had a valid basis for the claim, not just whether the claim is correct.
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Choose the path that matches your current situation
You have no hallucination detection yet
You are doing some checking but hallucinations still slip through
Detection is working but you want fewer false positives
You have learned how to catch AI falsehoods before they reach customers. The natural next step is understanding how to block harmful content entirely with output guardrails.