Evaluation of LLM Outputs: Metrics, Tests, and Human Feedback
Explore efficient methods (PEFT/LoRA) that make fine-tuning accessible to engineers without massive GPU clusters.
Previously, we built a pipeline that retrieves documentation and generates structured answers grounded in that data. At this point, the system runs end-to-end. Requests return 200 OK. Latency and cost are observable.
None of that tells us whether the system is correct.
In traditional software engineering, correctness is binary. If a function computes 2 + 2, we assert the result is 4. Any deviation fails the test.
In generative systems, correctness is semantic. If the expected answer is set the Authorization header, and the model outputs the API key is passed via request headers, a strict string comparison fails even though the meaning is identical. Conversely, an answer may appear linguistically correct while being grounded in the wrong document or fabricating details entirely.
This ambiguity often pushes teams into an
A developer modifies a prompt, manually inspects a small sample of outputs, concludes the change is acceptable, and merges it. Days later, users report regressions in seemingly unrelated queries. The system remained syntactically valid, but its behavior drifted semantically.
This lesson addresses that problem by shifting evaluation from subjective inspection to automated, quantitative checks.
It introduces the LLM-as-a-Judge pattern, along with metrics commonly used to evaluate retrieval-augmented generation (RAG) systems. By the end of this lesson, evaluation is treated as a blocking quality gate in the LLMOps workflow. ...