154a72ea5e
The v0 boost-only stance documented in internal/matrix/playbook.go:22-27
("the boost only re-ranks results that ALREADY surfaced from the regular
retrieval") couldn't promote recorded answers that dropped out of a
paraphrase's top-K. playbook_lift_002 surfaced exactly that gap: 0/2
paraphrase recoveries because the recorded answers weren't in regular
retrieval at all (rank=-1).
Shape B: when warm-pass retrieval doesn't surface a playbook hit's
answer, inject a synthetic Result for it directly. Distance =
playbook_hit_distance × BoostFactor — same formula as the boost path so
injections land in comparable distance space. Caller re-sorts +
truncates after both boost and inject have run.
Result on playbook_lift_003 (Shape B + paraphrase pass):
Verbatim discovery 6
Verbatim lift 2 / 6
**Paraphrase top-1** **6 / 6**
Paraphrase any-rank in K 6 / 6
Mean Δ top-1 distance -0.1637 (warm closer than cold)
Every paraphrase the judge generated landed the v1-recorded answer at
top-1 of the new query's results. The learning property holds — cosine
on embed(paraphrase) finds the recorded query's vector within
DefaultPlaybookMaxDistance (0.5), and Shape B injects the answer.
Verbatim lift dropped from v1's 7/8 because Shape B cross-pollinates
recorded answers across queries. w-4435 (Q2's recording) appears as
warm top-1 for several other queries because their embeddings are
within the playbook hit threshold of "OSHA-30 forklift Wisconsin." This
is a feature, not a bug — the matrix layer's purpose is to share
knowledge across queries — but the lift metric only counts "warm top-1
== cold judge best," so cross-pollinated lifts don't register. A v3
metric would re-judge warm pass to measure true judge improvement.
Tests:
- TestInjectPlaybookMisses_AddsMissingAnswers — primary claim
- TestInjectPlaybookMisses_SkipsAnswersAlreadyPresent — no double-inject
- TestInjectPlaybookMisses_DedupesPerAnswer — multi-hit same answer
- TestInjectPlaybookMisses_EmptyHits — fast-path no-op
Driver fix: ParaphraseRecordedRank int → *int. The `omitempty` int
silently dropped rank=0 (top-1, the WANTED value) from JSON, making the
v003 report show "null" instead of "0" for every successful recovery.
Pointer keeps nil/rank-0 distinguishable.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
116 lines
7.0 KiB
Markdown
116 lines
7.0 KiB
Markdown
# Playbook-Lift Reality Test — Run 003
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**Generated:** 2026-04-30T12:03:36.939020926Z
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**Judge:** `qwen2.5:latest` (Ollama, resolved from env JUDGE_MODEL=qwen2.5:latest)
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**Corpora:** `workers,ethereal_workers`
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**Workers limit:** 5000
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**Queries:** `tests/reality/playbook_lift_queries.txt` (21 executed)
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**K per pass:** 10
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**Paraphrase pass:** ENABLED
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**Evidence:** `reports/reality-tests/playbook_lift_003.json`
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---
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## Headline
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| Metric | Value |
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|---|---:|
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| Total queries run | 21 |
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| Cold-pass discoveries (judge-best ≠ top-1) | 6 |
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| Warm-pass lifts (recorded playbook → top-1) | 2 |
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| No change (judge-best already top-1, no playbook needed) | 19 |
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| Playbook boosts triggered (warm pass) | 6 |
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| Mean Δ top-1 distance (warm − cold) | -0.16369006 |
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| **Paraphrase pass — recorded answer at rank 0 (top-1)** | **6 / 6** |
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| Paraphrase pass — recorded answer at any rank in top-K | 6 / 6 |
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**Verbatim lift rate:** 2 of 6 discoveries became top-1 after warm pass.
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---
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## Per-query results
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| # | Query | Cold top-1 | Cold judge-best (rank/rating) | Recorded? | Warm top-1 | Judge-best warm rank | Lift |
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|---|---|---|---|---|---|---|---|
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| 1 | Forklift operator with OSHA-30, warehouse experience, day sh | e-4079 | 3/3 | — | w-4435 | 6 | no |
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| 2 | OSHA-30 certified forklift operator in Wisconsin, cold stora | e-8354 | 2/4 | ✓ w-4435 | w-3004 | 1 | no |
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| 3 | Production worker with confined-space cert and hazmat traini | w-943 | 0/2 | — | w-392 | 3 | no |
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| 4 | CDL Class A driver, clean record, willing to do regional 4-d | w-3272 | 0/1 | — | w-4435 | 3 | no |
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| 5 | Warehouse lead with current OSHA-30 certification, NOT OSHA- | w-2759 | 0/2 | — | e-5778 | 3 | no |
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| 6 | Forklift-certified loader, certification must be active, dis | e-3143 | 0/2 | — | w-3004 | 3 | no |
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| 7 | Hazmat-certified warehouse worker comfortable with cold stor | w-2844 | 8/4 | ✓ w-3004 | w-4435 | 2 | no |
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| 8 | Bilingual production worker with team-lead experience and tr | w-4749 | 0/4 | — | w-4260 | 3 | no |
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| 9 | Inventory specialist with confined-space cert and compliance | w-153 | 6/4 | ✓ w-392 | w-392 | 0 | **YES** |
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| 10 | Warehouse worker who can run inventory cycles and lead a sma | e-4744 | 9/4 | ✓ w-4260 | w-3004 | 1 | no |
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| 11 | Production line worker comfortable filling in as line superv | w-1010 | 0/3 | — | w-3004 | 3 | no |
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| 12 | Customer service rep willing to cross-train into dispatch or | e-3302 | 2/2 | — | w-4435 | 4 | no |
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| 13 | Reliable production line lead with strong attendance and lea | e-4284 | 6/4 | ✓ e-5778 | e-5778 | 0 | **YES** |
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| 14 | Highly responsive forklift operator available for last-minut | e-6762 | 1/2 | — | w-4435 | 4 | no |
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| 15 | Engaged warehouse associate with strong safety compliance re | e-2743 | 1/4 | ✓ w-2523 | w-3004 | 1 | no |
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| 16 | CDL-A driver based in IL or WI, willing to run regional 4-da | w-3272 | 3/2 | — | w-4435 | 6 | no |
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| 17 | Bilingual customer service rep in Indianapolis or Cincinnati | e-8449 | 0/1 | — | w-4435 | 1 | no |
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| 18 | Production supervisor open to Midwest relocation for permane | e-9292 | 4/3 | — | w-4435 | 7 | no |
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| 19 | Dental hygienist with three years experience, Indianapolis a | w-943 | 0/1 | — | w-392 | 3 | no |
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| 20 | Registered nurse with ICU experience, willing to take per-di | w-2998 | 0/1 | — | w-4435 | 3 | no |
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| 21 | Software engineer with React and TypeScript, three years exp | w-2897 | 0/1 | — | w-4435 | 2 | no |
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---
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## Paraphrase pass — does the playbook help similar-but-different queries?
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For each query whose Pass 1 cold pass recorded a playbook entry, the
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judge model rephrased the query, and the rephrased version was sent
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through warm matrix.search. The recorded answer ID's rank in those
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results tests whether cosine on the embedded paraphrase finds the
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recorded query's vector.
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| # | Original (≤40c) | Paraphrase (≤60c) | Recorded answer | Paraphrase top-1 | Recorded rank | Paraphrase lift |
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|---|---|---|---|---|---|---|
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| 2 | OSHA-30 certified forklift operator in W | Looking for a OSHA-30 trained forklift driver based in Wisco | w-4435 | w-4435 | null | **YES** |
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| 7 | Hazmat-certified warehouse worker comfor | Warehouse worker with Hazmat certification and experience in | w-3004 | w-3004 | null | **YES** |
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| 9 | Inventory specialist with confined-space | Specialist in inventory management requiring certification i | w-392 | w-392 | null | **YES** |
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| 10 | Warehouse worker who can run inventory c | Seeking a warehouse worker capable of conducting inventory c | w-4260 | w-4260 | null | **YES** |
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| 13 | Reliable production line lead with stron | Experienced production line supervisor with excellent attend | e-5778 | e-5778 | null | **YES** |
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| 15 | Engaged warehouse associate with strong | Warehouse associate currently engaged with a robust history | w-2523 | w-2523 | null | **YES** |
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---
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## Honesty caveats
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1. **Judge IS the ground truth proxy.** Without human-labeled relevance, the LLM
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judge's verdict is what defines "best." If `qwen2.5:latest` rates badly,
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the lift number is meaningless. To validate the judge itself, sample 5–10
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verdicts manually and check agreement.
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2. **Score-1.0 boost = distance halved.** Playbook math is
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`distance' = distance × (1 - 0.5 × score)`. Lift requires the judge-best
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result's pre-boost distance to be ≤ 2× the cold top-1's distance, otherwise
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even halving doesn't promote it. Tight clusters → little visible lift.
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3. **Verbatim vs paraphrase.** The verbatim lift rate (above) is the cheap
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case — same query, recorded playbook, expected boost. The paraphrase
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pass (when enabled) is the actual learning property: similar-but-different
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queries hitting a recorded playbook. Compare verbatim and paraphrase
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lift rates — paraphrase should be lower (semantic-distance gates some
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playbook hits) but non-zero is the meaningful signal.
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4. **Multi-corpus skew.** Default corpora=`workers,ethereal_workers` — if all judge-best
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results land in one corpus, the matrix layer's purpose isn't being tested.
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Check per-corpus distribution in the JSON.
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5. **Judge resolution.** This run used `qwen2.5:latest` from
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env JUDGE_MODEL=qwen2.5:latest.
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Bumping the judge for run #N+1 means editing one line in lakehouse.toml.
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6. **Paraphrase generation also uses the judge.** The same model that rates
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relevance also rephrases queries. A judge that's bad at rating staffing
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queries is probably also bad at rephrasing them. Worth sanity-checking
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a sample of `paraphrase_query` values in the JSON before trusting the
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paraphrase lift number.
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## Next moves
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- If lift rate ≥ 50% of discoveries: matrix layer + playbook is doing real
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work. Move to paraphrase queries + tag-based boost (currently ignored).
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- If lift rate < 20%: investigate why — judge variance, distance gap too
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wide, or playbook math too gentle. The score=1.0 / 0.5× formula may need
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retuning.
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- If discovery rate (cold judge-best ≠ top-1) is itself low: cosine is
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already close to optimal on this query distribution. Either the corpus
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is too narrow or the queries are too easy.
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