LLM Best Practices

Evaluation harness

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This is LLM Best Practices (llmbestpractices.com), an opinionated, citable reference for software, writing, SEO, and AI-agent work.

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Overview

An evaluation harness is the repeatable runner for LLM tests: it loads cases from a golden-set, calls the system under test, scores each output, aggregates metrics, and reports them. Run it in CI on every prompt, model, or retrieval change so a regression fails a check instead of reaching users.

A minimal harness has five parts: a dataset loader, a runner (with retries and rate limiting), a scorer, an aggregator (mean score, per-category breakdown, pass@k), and a reporter. Define the success metric before looking at results. Start with exact match or regex scoring for classification and extraction, which is cheap and deterministic; add llm-as-judge scoring for open-ended output, checked against a human-graded sample.

Existing options include promptfoo (open source; OpenAI announced its acquisition in March 2026), EleutherAI’s lm-evaluation-harness, Braintrust, and LangSmith.

Example

def run_eval(cases, template):
    scores = []
    for case in cases:
        out = client.messages.create(
            model="claude-sonnet-5-5", max_tokens=256,
            messages=[{"role": "user", "content": template.format(**case)}],
        ).content[0].text.strip()
        scores.append(out == case["expected"])
    return {"accuracy": sum(scores) / len(scores), "n": len(scores)}
  • golden-set: the test data the harness runs; its quality limits the eval’s validity.
  • llm-as-judge: scoring for open-ended output.
  • evaluation: metric choice and evaluation strategy.
  • prompt-evals: regression testing for prompts.
  • hallucination: a metric commonly measured in a harness.

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