Senior Engineer
Senior engineer at ByteTuned, leading production AI builds and modernizations.
4 articles
Evaluate 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.
You 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.
A field report on scoping, retrieval quality, and the evals that let us put a retrieval-grounded assistant in front of real users — fast.
Most 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.
