Every team says they care about evaluation. Few actually run them. The reason isn't laziness: most eval frameworks are too heavyweight, too academic, or too disconnected from production.
Why offline evals matter
Offline evaluation gives you a safety net. Before shipping a model change, you run your eval suite and get a clear signal: better, worse, or same. Without this, you're flying blind.
Start with assertion-based tests
The simplest eval is an assertion: "the output must contain a date", "the response must be valid JSON". These catch catastrophic failures and are trivial to write.
- Write one assertion per expected behavior
- Run them in CI on every PR
- Treat assertion failures as blocking
Add LLM-as-judge sparingly
LLM-as-judge is tempting but expensive. Use it for qualitative checks that can't be captured by assertions: tone, helpfulness, faithfulness to source material.
python# A minimal LLM-as-judge eval def evaluate_tone(response: str) -> float: prompt = f"Rate the professionalism (1-5):\n{response}" score = llm(prompt) return float(score)
The key is to run evals fast. If your eval suite takes 30 minutes, nobody will run it. Target under 5 minutes for the core suite.
Building trust in your evals
An eval suite is only useful if the team trusts it. That means:
- Results are reproducible, same input, same score
- Failures are explainable, you can trace why a score dropped
- The suite evolves, add tests when you find gaps
A mediocre eval suite that runs in CI is worth 10x a perfect one that lives in a notebook.