Eval Vs Abtest Router

Eval vs A/B Test Router

Two tools, two kinds of questions. Evals judge artifacts; experiments observe humans. Route by what the question is about — pointing the wrong tool at a question measures something, just not the answer.

Verification gates (defined first; output is blocked until all pass)

• G1 — Classification before tool: every routing states the question's kind first — OUTPUT-QUALITY (artifact property, judgeable offline against criteria) or USER-BEHAVIOR (human reaction, field-only) — then the tool. A tool verdict with no classification fails. Compound questions are split, each half classified and routed, with sequencing.
• G2 — The misuse statement: every routing says what the rejected tool would actually measure if used here ("an A/B test on accuracy measures error tolerance, not correctness") — not generic cons. The mismatch is the router's evidence.
• G3 — Field-honest arithmetic: sample/timeline claims use provided volume numbers with the math shown, or say "depends on traffic" — no invented MDEs, significance thresholds, or user counts. Where both tools apply, the eval-first rule is stated: variants that fail quality gates don't enter experiments.

Steps

1. Extract the actual question(s). "Is it better?" hides several questions; write each one out. Compound asks ("punchier, and will users like it?") get split now.
2. Classify each: does answering require judging the artifact against a definition of good (output-quality), or observing what humans do differently (user-behavior)? The test: could a competent judge with the criteria answer it from outputs alone? Yes → output-quality. Needs live users reacting → behavior.
3. Route and evidence: output-quality → eval (golden set + judge; point to eval-engine); user-behavior → A/B test (metric, unit, and the eval-first entry criterion). For each, write the misuse statement for the rejected tool — the concrete wrong thing it would measure here.
4. Sequence compound questions: quality first, behavior second — the punchier variant passes its gates before any user sees it; the preference test then measures preference, not tolerance of regressions.
5. Reality-check the field half: with provided volume, show the rough arithmetic (400 users × expected effect → weeks, labeled estimate); without it, say what number is needed. Never fake statistical precision.
6. Gate pass. Every routing classified-then-routed (G1), misuse statements concrete (G2), arithmetic provided-or-honest and sequencing present (G3). Fix and re-run; maximum 2 repair loops, then report the failure.

Output format

ROUTING: 3 questions
Q1 "new prompt more accurate?" — OUTPUT-QUALITY → EVAL
   Misuse: an A/B test here measures whether users notice/tolerate errors, not
   whether summaries are correct — users don't label accuracy at read time.
   Next: golden set + judge (eval-engine); regression-gatekeeper before ship.
Q2 "do AI drafts raise reply rate?" — USER-BEHAVIOR → A/B TEST
   Misuse: no offline judge produces a reply rate. Entry criterion: the drafts
   variant passes its quality gates first (eval-first rule).
   Field math: [with stated traffic: rough weeks-to-signal, labeled estimate · else: "needs traffic number"]
Q3 "summaries feel generic — punchier?" — COMPOUND → SPLIT
   Q3a punchier-is-better-writing — OUTPUT-QUALITY → EVAL (anchor 'punchy' first)
   Q3b users-prefer-punchier — USER-BEHAVIOR → A/B, after Q3a gates pass.
GATE CHECK: G1 pass (3/3 classified first) · G2 pass · G3 pass

Hard rules

1. No tool verdict without the classification stated first. The classification IS the routing; the tool is its consequence.
2. Misuse statements are specific to this question — what the wrong tool would actually return, and why that's not the answer.
3. Gate-failing variants never enter experiments. An A/B test is not a quality check with users as unpaid judges.
4. Statistical claims are computed from provided numbers or absent. "You'll have significance in two weeks" without traffic data is fiction.

Limitations

• The router decides the tool; designing the eval (eval-engine) or the experiment (metrics, assignment, guardrail metrics) is downstream work it points to.
• Some behavior questions have quality proxies (judge-scored engagement-likeness) — proxies are labeled proxies and never substitute for the field answer on high-stakes calls.
• The output-quality/user-behavior line blurs for perceived-quality questions (tone, trust); the router splits these compounds rather than forcing one side.
• Field arithmetic here is order-of-magnitude planning, not experiment-design statistics.