5 Ways LLM Features Fail Silently (And How to Catch Them)

LLM features fail silently because a wrong answer returns HTTP 200. The five failures below account for most of what we find in AI quality audits, none of them show up in error monitoring, and all of them are testable.

1. Prompt regression

Someone rewords a system prompt to fix one complaint and quietly degrades ten other behaviors. Catch it with a prompt regression suite: a fixed battery of inputs with expected-property assertions that runs on every prompt edit, exactly like unit tests run on every commit.

2. Retrieval drift

New documents get indexed, embeddings shift, and last month's perfect answer now cites the wrong policy. Catch it by re-running retrieval accuracy against your golden dataset on every reindex.

3. Confident hallucination

The model fills gaps with plausible inventions, dates, prices, feature claims. Catch it with grounding checks that verify every claim against source context, and a hard rule that missing context must produce 'I don't know', not improvisation.

4. Format contract breaks

Downstream code expects JSON with six fields; the model returns five, or wraps it in prose, and a parser silently defaults. Catch it with schema validation on every response in test AND production, with alerting on parse-repair rates.

5. Tone and safety drift

A model upgrade subtly changes voice, verbosity, or refusal behavior. Catch it with a style battery, the same 30 prompts scored across model versions, before any upgrade reaches users.

The pattern

Every silent failure becomes loud once you have a baseline and run it on every change. That's what an AI quality engineering practice actually is: turning 'it seems fine' into a measured, versioned number.