Learning outcomes
- Classify factual failures
- Measure evidence support
- Select grounding controls
Mental model
A hallucination is an unsupported or incorrect model claim. Grounding reduces the allowed claim space by connecting outputs to trusted evidence, but retrieval alone does not guarantee support.
Theory
Failures can originate in missing retrieval, irrelevant retrieval, conflicting sources, synthesis beyond evidence, stale data, or incorrect tool results. Evaluate these stages separately. Useful controls include authoritative source selection, abstention, claim decomposition, citation support checks, deterministic tools, and human escalation.
Alternatives and trade-offs
Closed-book generation is simplest, RAG supplies documents, tools supply structured truth, and extractive responses reduce transformation when precision dominates fluency.
Failure modes and misconceptions
Do not use a citation count as factuality, force an answer when evidence is absent, or label every disagreement a model failure without checking source quality.
Knowledge check
Which measurements distinguish a retrieval failure from a generation failure?
Decision scenario
When a benefits answer is unsupported, record whether the correct passage was retrieved before changing the prompt or model.
Learning outcomes
- Explain Grounding and Hallucination as a system mechanism rather than a slogan.
- Compare its alternatives, trade-offs, and production failure modes.
- Apply the concept to a decision and identify evidence that would validate it.
Trade-offs
Using Grounding and Hallucination can improve capability or control, but it also introduces cost, latency, complexity, and failure modes that must be measured against an explicit objective.
Evidence assessment
Theory and decision mastery
Decision scenario
A production team must adopt Grounding and Hallucination while meeting quality, latency, security, and operating constraints.
Which decision process is most defensible?
Relationships
Grounding failures are evaluated against retrieved evidence and claims.
Grounding quality needs explicit cases metrics and rubrics.
Primary sources
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks — arXiv, verified 2026-07-16
- Evaluation Best Practices — OpenAI, verified 2026-07-16