LLM evaluation framework
A working taxonomy of how multi-turn conversations break, and the datasets built to catch it.
- Evaluation datasets
- Failure taxonomy
- Prompt engineering
“The model seems worse at X” isn't actionable without a way to say precisely where multi-turn conversations break, ambiguity, lost context, unsupported claims, failed intent recognition, and without a repeatable way to measure it before and after a change.
Treat evaluation as a product surface, not a one-off script. Build a shared vocabulary for failure modes so an evaluator, a prompt engineer, and a researcher can point at the same transcript and agree on what went wrong.
Evaluation datasets and testing frameworks measuring response quality, factual grounding, hallucination rate, and task completion across multi-step planning and tool-use scenarios. A working taxonomy of interaction-level failure modes, ambiguity, failed intents, context loss, unsupported responses, built from analyzing human-AI transcripts, feeding back into prompt and model improvements documented as guidelines for other teams.
A reusable evaluation methodology and failure taxonomy adopted across teams, not a single product, proof of systems thinking rather than a shipped screen.