AI engineering
evaluationllmtesting
Updated June 2026
Design an evaluation strategy for an LLM feature before you ship it.
Why it works: Asking for bad output examples grounds the eval in real failure modes; ranking dimensions forces prioritization over a laundry list.
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Help me design an evaluation for an LLM-powered feature.
The feature: [what the LLM does, e.g. “summarizes support tickets”, “generates SQL from natural language”]
What good output looks like: [describe quality criteria, even roughly]
What bad output looks like: [failure modes you’ve seen or worry about: hallucination, format breaks, tone, missed edge cases]
Scale and constraints: [how often this runs, latency/cost budget for evaluation, whether you have labeled data]
Please propose:
- Eval dimensions: the 3-5 properties worth measuring, ranked by how much they matter for this feature
- For each dimension: whether it’s best checked by exact match, heuristics, an LLM judge, or human review, and why
- A starter dataset plan: where the test cases should come from and how many are enough to start
- The tripwires: which metrics should block a deploy vs. just be tracked
- What I’m probably overweighting: common eval mistakes for this type of feature