Engineering PracticeEvaluate a RAG system in two halves: did retrieval fetch the right context, and did the model answer faithfully from it? Measure retrieval with context precision and recall, generation with faithfulness and answer relevancy — against a fixed set of test cases.
Engineering PracticeYou cannot fully eliminate hallucinations, but you can drive them down with layers: ground the model in retrieved facts, constrain it with low temperature and structured output, validate with guardrails and an LLM judge, and measure the rate with evals.
Case NotesA field report on scoping, retrieval quality, and the evals that let us put a retrieval-grounded assistant in front of real users — fast.
Engineering PracticeMost production LLM bills can be cut 60–80% without hurting quality, because most requests are easy and do not need your most expensive model. The big levers: route to smaller models, cache repeated prompts, right-size, and trim context.