LLM Evaluation

Prompt and model changes have unpredictable effects. Systematic eval prevents shipping regressions and quantifies improvements for stakeholders.

LLM eval is standardized testing for models — same questions, scored rubrics, comparable grades across versions.

Scenario

Eval set of 200 FAQ pairs; new model scores 87% exact match vs 91% baseline — investigate 26 regressions before release.

Solution

In Agent Evaluation & Observability, apply LLM Evaluation to this scenario: Eval set of 200 FAQ pairs; new model scores 87% exact match vs 91% baseline — investigate 26 regressions before release. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to LLM Evaluation (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where LLM Evaluation happens in the code.

1def exact_match(pred, ref):  # define a reusable function2    return pred.strip().lower() == ref.strip().lower()  # return the result3
4scores = [exact_match(agent(q), ref) for q, ref in eval_set]5print(f"EM: {sum(scores)/len(scores):.1%}")  # show output for debugging

• Treating LLM Evaluation as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for llm evaluation

Quick recap — the most important points from this module.