Why Evaluations Matter

When you ship a feature backed by a deterministic function, a failing unit test tells you exactly what broke. When you ship a feature backed by a language model, the output is probabilistic and context-dependent — you cannot know whether it is working correctly without a deliberate measurement strategy. That measurement strategy is what practitioners call an eval.

The core problem: vibes don't scale

Early in a project, developers iterate quickly by reading a handful of sample outputs and deciding whether they look right. This works when the team is small and the task is simple, but it breaks down the moment you need to compare two prompts systematically, track quality across model upgrades, or hand the system off to someone who wasn't there for the original vibe check. Informal intuition is not reproducible and cannot catch regressions.

What an eval actually measures

• Correctness: does the output match a known-good reference or satisfy a rubric?
• Consistency: does the same input reliably produce outputs of equivalent quality?
• Regression safety: does a prompt or model change preserve existing behavior?
• Edge-case coverage: does the system handle unusual inputs without degrading badly?
• Safety compliance: does the output stay within defined content and behavior boundaries?

The eval spectrum

Evals range from simple string matching on the left to full human annotation on the right. In between sit automated heuristics, embedding-similarity scores, reference-based metrics like ROUGE, and LLM-as-judge. The right point on the spectrum depends on the task: exact-answer tasks (code execution, fact lookup) can use deterministic checks; open-ended tasks (summarization, conversation) need human or model judgment.

# A minimal eval loop
results = []
for example in golden_dataset:
    output = call_model(example["input"])
    score = score_output(
        output=output,
        reference=example["expected"],
        rubric=example.get("rubric"),
    )
    results.append({"id": example["id"], "score": score})

pass_rate = sum(r["score"] >= 0.7 for r in results) / len(results)
print(f"Pass rate: {pass_rate:.1%}")