How I Evaluate LLM Workflows When Correctness Is Fuzzy

Not every LLM task has one right answer

Some AI workflows are easy to evaluate because the answer is binary: a schema is valid, a tool call is allowed, or a required field is present. Many useful engineering workflows are fuzzier. A failure-investigation summary, test-plan critique, or risk-ranked impact summary can be useful without having a single canonical answer.

Separate format from judgment

I try to split evaluation into layers. First, did the system produce the required shape? Second, did it respect the boundaries? Third, was the judgment actually useful to a human reviewer?

Use structured expectations

• Required fields and schema validation for output shape.
• Specific evidence expectations for grounded answers.
• Known failure-mode examples for recurring weaknesses.
• Human review rubrics for usefulness, specificity, and false confidence.

Combine eval types

Deterministic checks are good for schema, required sections, unsafe actions, and missing review points. Model-graded checks can help judge clarity or relevance, but they need calibration. Human review remains essential when the workflow involves expert judgment.

Track failure modes over time

Prototype success is not deployment readiness. I want to know where the workflow fails: missing evidence, overconfident conclusions, irrelevant retrieval, weak edge-case handling, bad action boundaries, or outputs that look plausible but do not help the user.

Make uncertainty visible

The system should say what it knows, what evidence supports it, and what remains uncertain. In engineering workflows, honest uncertainty is often a feature.