Public benchmarks (MMLU, HumanEval, and others) measure genuinely general capability, but they frequently don’t predict how a model will actually perform on your own genuinely specific, real task — building your own evaluation is worth the real, upfront effort for anything remotely consequential.
A Practical Approach
test_cases = [
{"input": "Summarize this support ticket: ...", "expected_key_points": [...]},
{"input": "Classify this ticket's urgency: ...", "expected_label": "high"},
# ideally 50-200+ genuinely REAL examples drawn directly from your actual use case
]
def evaluate_model(model, test_cases):
results = []
for case in test_cases:
output = model.generate(case["input"])
score = score_output(output, case) # a rubric, or another model as a judge, or human review
results.append({"case": case, "output": output, "score": score})
return results
def compare_models(models, test_cases):
return {model.name: evaluate_model(model, test_cases) for model in models}
Why Building Your Own Eval Set Genuinely Matters
A model that scores genuinely well on general benchmarks might perform meaningfully worse on your specific domain’s own particular jargon, edge cases, or genuinely required output format — the only truly reliable way to know how a model will actually perform for your real, specific use case is testing it directly against representative, real examples drawn from that actual use case, not extrapolating confidently from a genuinely unrelated general-purpose public benchmark.
Using an LLM as a Judge
For subjective quality dimensions genuinely hard to score with a purely simple, mechanical rule, using a separate, strong LLM to score outputs against a clearly written rubric is an increasingly common, genuinely practical pattern — imperfect, but frequently meaningfully more scalable and consistent than exclusively relying on slow, expensive manual human review for absolutely every single evaluation.