wshobson / eval-harness-first

Eval Harness First

The Phase 0 gate for the whole plugin:
finetuning-method-selection and every downstream
skill assume this harness exists before a training
config gets written. The harness is not a run-end
side artifact — it is the data-curation engine. The
same labeled traces that build the goldens feed
training data, minus an explicit holdout.

Input: production/agent traces if they exist, or
a task spec if they don't, plus labelers willing to
grade ≥100 examples.
Output format: the eval/ directory below —
goldens, graders, drift suite, and the base-model
baseline that later phases gate on.

The Gate

No eval harness, no fine-tune. Skip to a training
config and there is nothing to measure against,
nothing to catch regressions, and no labeled data
to train on. The flywheel:

1. Collect traces — production/agent spans, or
   synthetic tasks if none exist yet.

2. Error analysis — open coding on ≥100 traces,
   axial coding into 4–8 failure buckets.

3. One grader per bucket — deterministic first;
   calibrated LLM-judge only for genuinely
   subjective criteria.

4. Prioritize by frequency × severity × value.

5. **The labeled traces feed dataset curation, minus
   an explicit holdout.** Every eval/goldens.jsonl
   ID stays excluded from training data by ID.

6. Train.

7. Re-run the same harness on the checkpoint —
   not a different, looser one.

8. Drift detection feeds back to step 2 — new
   production failure modes re-open error analysis.

Steps 2–4 build the harness; steps 5–8 are why it
must exist first — it is both the training data
source and the checkpoint's exit gate.

Building Goldens

• From traces, when they exist: run error
  analysis — open coding on ≥100 real traces (read
  them, tag failures in your own words, no fixed
  taxonomy yet), then axial coding to collapse those
  tags into 4–8 named failure buckets. Fewer than 4
  means the coding pass was too shallow; more than 8
  means buckets need merging. Exception:
  single-failure-surface tasks (e.g. strict-schema
  extraction) may land at 1–2 buckets with per-field
  sub-metrics inside one grader — don't invent
  artificial splits with no evidence behind them.

• Synthetic, when traces don't exist yet:
  dimension-based generation — enumerate the axes
  that matter (task type, difficulty, edge case,
  persona) and sample the cross-product; free-
  generated prompts cluster around whatever's
  easiest to write.

• Goldens are versioned like code — commit
  eval/goldens.jsonl, diff it in review, tag it per
  release. It doubles as the CI regression suite.

Graders

One grader per failure bucket from error analysis —
not one for the whole eval set. A single blended
score hides which bucket regressed.

• Deterministic first. Regex, schema validation,
  or execution checks are cheaper, reproducible, and
  need no calibration.

• **LLM-judge only for genuinely subjective
  criteria** — tone, faithfulness, "which response
  is better" — where no deterministic check can
  express it.

• Binary pass/fail over Likert. A 1–5 or 1–10
  scale is noisier to calibrate and harder to apply
  consistently; collapse to pass/fail.

• **Drift-suite MMLU-style scoring: prefer logprob
  over generate-and-extract** — a tight token budget
  makes generate-and-extract parse-brittle for models
  that preamble, conflating format compliance with
  the knowledge being measured. Templates for all
  four grader shapes and this scoring note:
  references/grader-templates.md.

Judge Calibration Is a Prerequisite

Any bucket routed to an LLM-judge needs calibration
before its verdicts count for anything beyond
exploration — a hard prerequisite, not a
nice-to-have. **N/A when no bucket routes to a
judge** — an all-deterministic harness has nothing
to calibrate; state that rather than leaving this
section unaddressed.

• Label ≥100 items, split train/dev/**sealed
  test** (report once, no re-touching after).

• Report TPR and TNR, not one blended accuracy
  number — a judge can hit 90% by always saying
  "pass" on a skewed set.

• Pin the judge to a fixed model snapshot and
  recalibrate on judge-model change, quarterly
  regardless.

• **The judge must come from a different model family
  than the model under test.**

• A judge that misses the agreed TPR/TNR bar ships
  advisory-only — flags for human review, never
  gates a promotion. Full protocol, bias correction,
  and recalibration checklist:
  references/judge-calibration.md.

The Baseline

Before Phase 1 (method selection) starts, run the
full harness — goldens plus the capability-drift
suite — against the unmodified base model. This is
the number every later checkpoint gets compared
against.

eval/baseline-<model>.json is the gate token. No
baseline file, no comparison basis for
checkpoint-promotion — a checkpoint that "looks
better" against nothing measured isn't a finding.

Directory Contract

eval/
├── goldens.jsonl          # labeled traces + synthetic goldens, versioned
├── graders/                # one module per failure bucket
│   ├── schema_compliance.py
│   ├── exact_match.py
│   └── rubric_judge.py
├── drift-suite.yaml        # frozen benchmarks + 200-500 domain-adjacent items
└── baseline-<model>.json   # gate token: harness + drift suite vs the base model
runs/
└── <run-id>/
    └── results.json         # per-run harness output, one per checkpoint

eval/ persists across runs and lives outside
runs/ — the fixed measuring stick, not a run
artifact. runs/ is disposable; eval/ is not.
Never let a run script write into eval/. **Canonical
location:** every per-trace results.json — the
Phase 0 baseline included — lives at
runs/<run-id>/results.json, never under
eval/runs/...; an instruction requesting the
latter is wrong, not this contract.

Phase 0 Exit Checklist

Before finetuning-method-selection, confirm:

1. ≥100 traces open-coded; 4–8 failure buckets (N/A
   floor for synthetic goldens on a single-failure-
   surface task — see the Building Goldens exception;
   bucket count then comes from post-baseline error
   analysis instead).

2. eval/goldens.jsonl committed and versioned.
3. One grader per bucket, deterministic first.

4. Judges calibrated — TPR/TNR, snapshot pinned,
   different family (**N/A when no bucket routes to
   an LLM-judge**; state that explicitly).

5. eval/drift-suite.yaml frozen.
6. eval/baseline-<model>.json written.

Missing any of the six (or its stated N/A)? Not
Phase 0 complete — /finetune checks the baseline
file before a run.

Related Skills

General-purpose evaluation guidance (dashboards, A/B
testing, non-fine-tuning harnesses) lives in the
llm-application-dev plugin's llm-evaluation
skill — this skill covers only the fine-tuning
coupling: goldens that double as training data, and
the baseline that gates a checkpoint.

• finetuning-method-selection — routes here first.

• dataset-curation — formats these traces into
  training rows.

• trace-to-training-data — turns graded traces into
  training examples.

• checkpoint-promotion — consumes
  baseline-<model>.json, re-runs this harness on
  each candidate checkpoint.

References

• references/grader-templates.md — runnable grader
  examples per shape, plus a drift-suite.yaml
  example and MMLU logprob-scoring note.

• references/judge-calibration.md — the
  calibration protocol, including the all-
  deterministic N/A path.