Detecting and Measuring Hallucinations
Learn practical techniques to detect when a RAG system invents facts not supported by retrieved context, and how to quantify hallucination rate as part of evaluation.
Detecting and Measuring Hallucinations is a free LLM Apps in Production (RAG + Vector DB + Caching) lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the LLM Apps in Production (RAG + Vector DB + Caching) learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
What Is a RAG Hallucination?
A hallucination is an answer that is fluent and confident but not supported by the retrieved context. In RAG, the cure is grounding: every claim should trace back to a source.
Measuring hallucination rate is essential to trust your system.
Faithfulness vs Correctness
Two different things to measure:
- Faithfulness — is the answer supported by the provided context?
- Correctness — is the answer factually true in the real world?
A RAG answer can be correct but unfaithful (right by luck) or faithful but wrong (the source was wrong).
Claim Decomposition
To check faithfulness, break the answer into atomic claims, then verify each against the context.
answer = 'Paris is the capital of France and has 5 million people.'
claims = [c.strip() for c in answer.replace(' and ', '. ').split('.') if c.strip()]
for c in claims:
print('CLAIM:', c)Context Entailment
For each claim, ask: does the retrieved context entail it? An NLI model or an LLM judge labels each claim as supported, contradicted, or not-mentioned.
- Supported = grounded
- Not-mentioned = potential hallucination
- Contradicted = definite error
LLM-as-Judge for Faithfulness
A common pattern: prompt a strong model with the context, the answer, and ask it to score whether the answer is fully supported. Calibrate the judge against human labels.
Computing Hallucination Rate
Hallucination rate = fraction of claims (or answers) that are unsupported.
labels = ['supported', 'supported', 'not_mentioned', 'contradicted']
bad = sum(1 for x in labels if x != 'supported')
rate = bad / len(labels)
print('Hallucination rate:', round(rate, 2))Citation Coverage
If your system outputs citations, you can measure citation coverage: the share of sentences that point to a retrieved chunk that actually supports them. Low coverage signals hallucination risk.
Detecting Missing Context
Many hallucinations happen because retrieval failed and the model filled the gap. Track cases where the context lacks the answer but the model still answered confidently instead of saying 'I do not know'.
A Simple Faithfulness Score
Aggregate per-claim labels into a single score per answer.
def faithfulness(labels):
return sum(1 for x in labels if x == 'supported') / len(labels)
print(faithfulness(['supported', 'supported', 'not_mentioned']))Reducing Hallucinations
Once measured, reduce hallucinations by:
- Improving retrieval recall
- Instructing the model to abstain when unsupported
- Requiring inline citations
- Post-hoc filtering of unsupported claims
Tracking Over Time
Add hallucination rate to your regular eval runs. Watch it on every prompt or model change so a regression is caught before it reaches users.
Quick Check
Test your understanding of faithfulness.
Recap
You learned to detect hallucinations by separating faithfulness from correctness, decomposing answers into claims, checking entailment against context with an LLM judge, and computing a hallucination rate. Track it over time and reduce it with better retrieval, abstention, and citations.
Frequently asked questions
Is the “Detecting and Measuring Hallucinations” lesson free?
Yes — the full text of “Detecting and Measuring Hallucinations” is free to read here on the web, and the LLM Apps in Production (RAG + Vector DB + Caching) course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the LLM Apps in Production (RAG + Vector DB + Caching) course, upgrade to CoddyKit PRO.
What will I learn in “Detecting and Measuring Hallucinations”?
Learn practical techniques to detect when a RAG system invents facts not supported by retrieved context, and how to quantify hallucination rate as part of evaluation. You practise LLM Apps in Production (RAG + Vector DB + Caching) with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start LLM Apps in Production (RAG + Vector DB + Caching)?
No prior experience is required. LLM Apps in Production (RAG + Vector DB + Caching) on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Detecting and Measuring Hallucinations” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this LLM Apps in Production (RAG + Vector DB + Caching) lesson?
Yes. Every LLM Apps in Production (RAG + Vector DB + Caching) lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Key Metrics for RAG Performance
- Developing Evaluation Benchmarks
- A/B Testing and User Feedback Loops
- Detecting and Measuring Hallucinations