Evaluation data should outlive the tool
Tools change quarterly. Evaluation datasets, traces, and outcomes are the asset that compounds — if you keep them.
Track lead — Evaluation, Governance & Auditability →Why this note exists
This note captures a technical observation from Yuauri's AI engineering work: tool evaluation, implementation planning, risk review, architecture decisions, or execution patterns. It may later become a full tool evaluation, implementation playbook, or internal delivery pattern.
Most teams treat evaluation as something that happens during procurement and then evaporates. The dataset is lost. The traces are not retained. The reasoning behind the original decision becomes folklore.
Evaluation data should be treated as a long-lived asset. Curated datasets, traced runs, decision records, and the criteria that drove a choice are kept independently of the tool that prompted them.
That matters beyond a single vendor relationship. Preserved evaluation data supports regression testing when prompts, models, or retrieval corpora change. It makes it possible to compare a replacement tool against the same business expectations instead of starting from zero. It gives auditors and technical leads a record of what was acceptable at the time of a decision. It reduces lock-in: the reasoning travels with the organization even when the product does not.
Over time, the same asset supports improvement. Teams can see whether a change fixed a known failure mode, whether a new model regressed on a risk case, and whether the system's behaviour still matches what stakeholders were told to expect.
When the next version of a tool ships — or when a credible alternative appears — the evaluation can be re-run quickly because the asset still exists.