Eval before feature
We write the labeled holdout set first, so 'done' has a definition before anyone writes the prompt.
← The K-Framework
★ Amplification · 10× ThroughputLayer 9 of 16Visual guide
PILLAR B · LAYER 03 · B.03Evaluation Engine.
Measure, benchmark, iterate.
What a CEO/CTO needs to know
Without an eval suite, 'the AI works' is an opinion. The eval bar is the number that turns a quality argument into an engineering task.
A release meets the eval bar before it ships. Below threshold, the gate stays shut.
[WHAT IT IS]
The engineer’s view, in plain language.
Without an eval suite, AI projects run on vibes. We build the eval before the feature. Every release passes through it and every model swap is gated on it. The eval is the contract between the team and the production system.
[HOW WE BUILD IT]
What “done right” looks like.
Gated on every change
The eval runs on every PR and production is gated on a threshold, so regressions close the gate instead of reaching the user.
Per-field, per-archetype
Precision and recall per field, pass rate per user archetype, plus a red-team set, so a single average cannot hide a broken case.
[MATURITY LADDER]
Where does your build sit?
Four rungs from absent to production-grade. Level 3 is the target, and the only one that survives a real production incident.
L0
Absent
No eval suite. Releases ship on a single test prompt and gut feel.
L1
Ad-hoc
A handful of example prompts are checked by hand before big releases.
L2
Managed
An eval set exists but does not gate releases, and coverage is uneven.
L3Target
Production-grade
Labeled holdout set, per-field and per-archetype scoring, red-team set, gated on every PR and reviewed quarterly.
[VALIDATE IT YOURSELF]
How to check it’s really there.
You do not need to read the code. Ask these questions and demand these artifacts. Vague answers are the finding.
★ Ask your team
- ?What is the number that says a release is good enough to ship?
- ?Does a quality regression block the release automatically?
- ?How many labeled cases back our eval, and do they cover the hard archetypes?
★ Demand to see
- A labeled holdout set with 500+ cases per intent
- A CI gate that blocks releases below the eval threshold
- Per-field precision/recall and a red-team test set
● WHAT L0 LOOKS LIKE
The failure mode, in production.
"We will do the eval next sprint." That sprint never arrives. Releases ship on one test prompt and intuition, regressions slip through, and the model behaves differently in production than in dev.
Want this layer audited in your stack?
We run the K-Framework against your AI build and hand you the gap list, ranked by what it will cost you in production.