LLM Evaluation and Observability

If prompts are your interface, evals and observability are your brakes and dashboard. You need a way to decide “Is this model/prompt better?” and a way to see “What actually happened?” – with artifacts you can replay in six months. In Python, that means rubric- and pairwise-style evaluations, prompt/response traces, guardrail tests that run like unit tests, and a couple of dashboards (W&B/MLflow or a simple JSONL + notebooks stack).