hallucination

Every customer asks how to stop the model making things up. The weak answer is 'better prompts.' The signal is knowing why models hallucinate and layering grounding, abstention, and measurement into a system you can defend.

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

A VLM that reads an invoice wrong and a VLM that reads it right and reasons wrong produce the same end-to-end score, and they need opposite fixes. Here is how to decompose a multimodal eval, build the set from real traffic, and probe for the hallucinations that accuracy never catches.

HyDE generates a fake answer to embed, which helps on vague queries but actively hurts on factual or out-of-domain ones. The signal is naming exactly 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 checking claim-by-claim entailment against the cited text, not just that a citation marker exists, and knowing what to do when grounding fails.

An answer can be correct and still unfaithful, stating 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.

Shipping an LLM feature safely is an evaluation problem before it is a model problem. The signal is a layered eval-plus-guardrail design with honest, segmented metrics, not a single 'safety classifier.' Here is how to measure and defend each failure mode.