Eval Harness Basics for LLM Features

Editorial note: this post is a practical pattern write-up, not a claim that every example here is already shipped in production by me.

You do not need a giant framework to start; you need a deterministic set of examples and pass/fail criteria.

Why this matters

Most automation failures are not caused by missing tools. They come from weak process boundaries, missing validation checkpoints, and unclear ownership when behavior drifts. I use this lens to keep systems maintainable under pressure.

Pattern I apply

• Curate representative prompts and edge cases.
• Score outputs with clear rubric or assertions.
• Fail CI when regressions cross agreed thresholds.

Failure modes I avoid

• Using only happy-path prompts.
• Comparing outputs by vibe instead of criteria.
• Treating eval drift as optional maintenance.

Practical recommendations

• Start with 20 high-value cases and grow from incidents.
• Separate quality gates from latency/cost gates.
• Review failing evals before merging behavior changes.

Honest scope

This is an evergreen backfill note designed to show how I reason and what I optimize for. It should be read as a practical playbook and editorial guidance, not as a blanket claim that every implementation detail has already been deployed in the same environment.

What I would test next

• Add a tiny proof workflow with synthetic inputs and failure injection.
• Measure whether the proposed guardrails reduce rework in a one-week run.
• Keep one small change log so improvements stay evidence-based.