Frequently Asked Questions
Common Questions About Preventing RAG Hallucination
Everything you need to know before registering.
Why do RAG systems still hallucinate even with retrieval? +
RAG systems hallucinate through several mechanisms: the model extrapolates beyond what retrieved documents actually say, low-confidence retrievals are treated as authoritative and cited incorrectly, the model generates plausible-sounding but uncited claims between retrieved facts, or the retrieval fails to find relevant documents and the model fills the gap with confabulation. The workshop addresses each mechanism with specific architectural safeguards.
What is the most effective single technique for preventing RAG hallucination? +
Citation-grounded generation is the most effective single technique: structuring the generation prompt to require that every factual claim in the response explicitly reference a retrieved source, then validating that each citation exists in the retrieval set and that the cited passage actually supports the attributed claim. This structural requirement eliminates the most common hallucination mechanism: confident claims generated from training data rather than retrieved evidence.
How do I validate that a citation actually supports the claim made in a RAG response? +
Claim-citation validation involves extracting the specific claim and its citation from the response, retrieving the cited source passage, and running an entailment check that determines whether the passage logically supports the claim. This can be implemented using a lightweight entailment model or by prompting an LLM to evaluate the support relationship. The workshop covers both approaches with their precision-latency tradeoffs.
How does confidence-gated generation prevent RAG hallucination? +
Confidence-gated generation uses retrieval confidence scores to determine whether to proceed with generation. When the highest-scoring retrieved document falls below a calibrated confidence threshold, the system returns an explicit uncertainty response rather than attempting to generate with insufficient knowledge grounding. This prevents the most dangerous form of hallucination: confident, well-formed answers to questions the system genuinely does not have reliable retrieval evidence for.
How do I detect RAG hallucination events in production to improve the system? +
RAG hallucination detection in production uses the Glass-Box logging layer to capture citation coverage metrics for every response. Low citation coverage responses are flagged for human review, which creates a labelled dataset of hallucination events. Analysing these events reveals the specific query types, knowledge base gaps, and retrieval failure patterns that cause hallucination, enabling systematic improvement of the RAG pipeline.
Can I prevent RAG hallucination without significantly increasing latency? +
Yes. The primary hallucination prevention techniques: citation-grounded generation prompts and citation presence validation (checking that cited sources exist in the retrieval set) add minimal latency. The more expensive technique: claim-citation entailment checking adds significant latency and is best applied selectively to high-stakes responses flagged by low citation coverage scores. The workshop covers a tiered validation approach that balances hallucination prevention with acceptable response latency.
