Evaluation & AI Reliability · core
Hallucination & Failure Detection
Unsupported claims, citations, abstention, tool errors, constraint violations, uncertainty, and escalation.
Mental model
Detect failures against evidence and task constraints. Require citations where possible, validate structured claims, calibrate abstention, and route uncertainty to tools or humans.
How to study Hallucination & Failure Detection
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative LLMs, Detecting hallucinations in large language models using semantic entropy (Nature, 2024), Extrinsic Hallucinations in LLMs (Lilian Weng) to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Hallucination & Failure Detection with AI Regression Testing, Quality, Cost & Latency Measurement. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Design exercise: Hallucination & Failure Detection and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Learn from primary sources
Practice and explain it back
Design exercise: Hallucination & Failure Detection
Unsupported claims, citations, abstention, tool errors, constraint violations, uncertainty, and escalation. Implement designOutline() returning non-empty values for: evidenceCheck, constraintCheck, escalation. Each value must name a concrete mechanism or decision.
Expected evidence: A design outline with evidenceCheck, constraintCheck, escalation plus an explicit failure mode or trade-off.
Open the interactive drill →Review prompts
- Name two mechanically different ways to detect a probable hallucination, and what each one misses.
Build evidence
Synthesize: Evaluation & AI Reliability
Build an evidence-backed evaluation and observability system for models, tools, and agents. Produce one working system, benchmark, or evidence-backed design that integrates the path.
- Implements or precisely models the core mechanisms from all three milestones
- Includes at least one injected failure or adversarial case and demonstrates recovery
- Reports quality, latency, resource, reliability, or usability measurements relevant to the domain
- Ships a concise architecture note explaining decisions, trade-offs, and remaining risks