This section is for practical AI engineering work: using models inside systems that generate candidates, obtain evidence, evaluate outcomes, and improve through feedback. The relevant unit is usually the whole job and its outcome, not an isolated model output.

Start with Working Assumptions for the current view that guides these notes.

For a more focused entry point, see Evaluation Harness and Platform for a dedicated page on evaluating LLM-based workflows and coding agents.