How to Detect Hallucinations in LLM Outputs

Hallucination detection is essential for any production LLM application. Here are practical methods to catch fabricated information before it reaches users.

How do you detect hallucinations in LLM outputs?

Detect hallucinations through: 1) Source verification - compare claims against retrieved documents in RAG systems, 2) Consistency checking - ask the same question multiple ways and compare answers, 3) Confidence analysis - monitor hedging language and uncertainty signals, 4) Automated classification - use AI classifiers trained to detect unsupported claims.

Method 1: Source Verification (RAG)

For RAG applications, compare the model's claims against retrieved source documents:

Log both retrieved sources and the final output
Check if specific claims appear in source documents
Flag responses that make assertions not found in sources
Pay attention to numbers, dates, and proper nouns

DriftRail's hallucination detection compares outputs against provided sources (retrievedSources in the ingest payload) to identify unsupported claims.

Method 2: Consistency Checking

Hallucinated information often changes between responses while facts remain stable:

Ask the same question with different phrasing
Compare specific details across responses
Inconsistent answers suggest hallucination

Method 3: Confidence Analysis

What are signs of AI hallucination?

Signs of hallucination include: specific facts without source backing, confident assertions about obscure topics, citations to non-existent papers or cases, internal contradictions within the response, details that change when asked again, and claims that contradict provided context or documents.

Monitor language patterns that indicate uncertainty vs. overconfidence:

Hedging language ("I think", "possibly", "might be") is appropriate for uncertain topics
Overconfidence on obscure topics is a red flag
Specific numbers and dates without sources warrant scrutiny

Method 4: Automated Classification

Use AI classifiers to analyze outputs at scale:

Train or use pre-built classifiers to detect hallucination patterns
Look for unsupported claims, fabricated citations, contradictions
Assign risk scores to prioritize human review

RAG and Hallucinations

Can RAG prevent hallucinations?

RAG (Retrieval-Augmented Generation) reduces but doesn't eliminate hallucinations. Models can still make claims not supported by retrieved documents, misinterpret source content, or hallucinate when sources don't contain relevant information. RAG requires verification that outputs are actually grounded in retrieved sources.

Industry Benchmarks

What hallucination rate is acceptable?

Acceptable hallucination rates depend on use case. Industry benchmarks show: finance targets under 5%, healthcare averages 12% without safeguards but can reach under 1% with proper controls, legal applications see 15%+ without RAG. High-stakes applications should target under 5% with human review for flagged outputs.

DriftRail provides built-in hallucination detection that runs on every event, comparing outputs against provided sources and flagging unsupported claims. Industry benchmarks help you compare your hallucination rates against peers in healthcare, finance, legal, and other sectors.