hallucination

Every customer wants to know how to stop the model inventing things. The weak reply is 'better prompts.' The signal is understanding why models hallucinate and stacking grounding, abstention, and measurement into a system you can defend.

Models are trained to be helpful, which quietly teaches them to never refuse. Restoring honest abstention is a system problem, not a prompt tweak. Here is the stack that actually shifts the refuse-when-unsure rate.

Summarization is the one task where the model was handed everything it needed and still made something up. The signal is separating intrinsic from extrinsic hallucination, knowing why ROUGE cannot see either, and gating on entailment before you ship.

HyDE produces a fake answer to embed, which helps on vague queries but actively hurts on factual or out-of-domain ones. Interviewers want to hear you name precisely when the hypothetical document misleads retrieval and which multi-draft and hybrid variants recover.

An LLM can cite a source that does not say what the answer claims. The signal is verifying claim-by-claim entailment against the cited text, not merely that a citation marker exists, and knowing what to do when grounding fails.

An answer can be correct yet still unfaithful, asserting things the retrieved context never said. The signal is scoring faithfulness against the context, not against truth, and knowing why LLM-judge faithfulness scores drift.

Retrieval is exonerated, so the bug lives in generation. There are four usual causes and they have different fixes, and the one nobody names is that the model was never given permission to say it does not know.

Asking a model 'is this answer hallucinated?' barely beats a coin flip. This is the build: claim extraction, a judge forbidden from using world knowledge, a denominator that excludes hedges, and one model call per claim, which decides where the check can run at all.

A model that never looks at the image can score respectably on VQA, which tells you most of what you need to know about the benchmark. Here is what the scores hide, and the one-line ablation that exposes it.

An eval suite that never varies the image will happily pass a model that has stopped looking at it. Start with the modality ablation, then pick metrics by what the system actually produces. Here is the full stack.

Getting an LLM feature to production safely is an evaluation problem first and a model problem second. What lands is a layered eval-plus-guardrail design with honest, segmented metrics rather than a single 'safety classifier.' Here is how to measure and defend each failure mode.

Both of the obvious moves cost you credibility: block the launch on principle, or ship quietly and hope. The answer that scores turns a scary percentage into a number of bad outputs per week and then changes what the argument is about.