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Adds an opt-in Pass 3 to the lift driver: for each query whose Pass 1 recorded a playbook, ask the judge to rephrase the query, then re-query with playbook=true and check whether the recorded answer surfaces in top-K. This is the test the v1 report's caveat #3 explicitly flagged as the actual learning-property gate (not the cheap verbatim case). Implementation: - New flag --with-paraphrase on the driver (default off). - New WITH_PARAPHRASE env in the harness (default 1, on for prod runs). - New paraphrase_* fields on queryRun + summary, // 0 fallback in jq so re-rendering verbatim-only evidence stays clean. - generateParaphrase() calls the same judge model with format=json and a tight schema; temperature=0.5 for variance without domain drift. - Markdown report adds a paraphrase per-query table (only when the pass ran) and an honesty caveat about judge-also-rephrases coupling. Run #002 result (reports/reality-tests/playbook_lift_002.{json,md}): Verbatim lift 2/2 (100% — Q7 + Q13, both stable from v1) Paraphrase top-1 0/2 Paraphrase any-rank in K 0/2 Both paraphrases dropped the recorded answer OUT of top-K entirely (rank=-1). This isn't a paraphrase-quality problem — qwen2.5's outputs preserved intent ("Hazmat-certified warehouse worker comfortable with cold storage" → "Warehouse worker with Hazmat certification and experience in cold storage"). It's the v0 boost-only stance documented in internal/matrix/playbook.go:22-27: the boost only re-ranks results that ALREADY surfaced from regular retrieval. If paraphrase's cosine retrieval doesn't include the recorded answer in top-K, no boost can promote it. The "Shape B" upgrade mentioned in the playbook.go comment — inject playbook hits directly even when they weren't in the top-K — is what would close this gap. The reality test surfaced exactly the gap the docs warned about. Worth filing as the next product gate. Run-to-run variance also visible: v1 had 8 discoveries, v2 had 2. HNSW insertion order + judge variance both contribute. Stability of Q7 and Q13 across both runs (lifted in v1 AND v2) is the most reliable signal in the dataset. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
112 lines
6.4 KiB
Markdown
112 lines
6.4 KiB
Markdown
# Playbook-Lift Reality Test — Run 002
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**Generated:** 2026-04-30T11:46:28.335370797Z
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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_002.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) | 2 |
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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) | 2 |
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| Mean Δ top-1 distance (warm − cold) | -0.011403477 |
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| **Paraphrase pass — recorded answer at rank 0 (top-1)** | **0 / 2** |
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| Paraphrase pass — recorded answer at any rank in top-K | 0 / 2 |
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**Verbatim lift rate:** 2 of 2 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-8290 | 0/4 | — | e-8290 | 0 | no |
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| 2 | OSHA-30 certified forklift operator in Wisconsin, cold stora | e-2580 | 7/3 | — | e-2580 | 7 | no |
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| 3 | Production worker with confined-space cert and hazmat traini | w-943 | 0/2 | — | w-943 | 0 | no |
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| 4 | CDL Class A driver, clean record, willing to do regional 4-d | w-2486 | 0/1 | — | w-2486 | 0 | no |
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| 5 | Warehouse lead with current OSHA-30 certification, NOT OSHA- | w-4278 | 2/2 | — | w-4278 | 2 | no |
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| 6 | Forklift-certified loader, certification must be active, dis | e-3143 | 0/2 | — | e-3143 | 0 | no |
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| 7 | Hazmat-certified warehouse worker comfortable with cold stor | e-898 | 2/4 | ✓ e-665 | e-665 | 0 | **YES** |
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| 8 | Bilingual production worker with team-lead experience and tr | w-4115 | 0/4 | — | w-4115 | 0 | no |
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| 9 | Inventory specialist with confined-space cert and compliance | w-1971 | 2/3 | — | w-1971 | 2 | no |
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| 10 | Warehouse worker who can run inventory cycles and lead a sma | e-8132 | 0/4 | — | e-8132 | 0 | no |
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| 11 | Production line worker comfortable filling in as line superv | w-2558 | 0/3 | — | w-2558 | 0 | no |
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| 12 | Customer service rep willing to cross-train into dispatch or | e-1349 | 1/2 | — | e-1349 | 1 | no |
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| 13 | Reliable production line lead with strong attendance and lea | e-6006 | 5/4 | ✓ e-5778 | e-5778 | 0 | **YES** |
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| 14 | Highly responsive forklift operator available for last-minut | e-6198 | 0/4 | — | e-6198 | 0 | no |
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| 15 | Engaged warehouse associate with strong safety compliance re | w-2008 | 0/4 | — | w-2008 | 0 | no |
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| 16 | CDL-A driver based in IL or WI, willing to run regional 4-da | w-542 | 6/2 | — | w-542 | 6 | no |
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| 17 | Bilingual customer service rep in Indianapolis or Cincinnati | e-4545 | 0/1 | — | e-4545 | 0 | no |
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| 18 | Production supervisor open to Midwest relocation for permane | e-3001 | 7/2 | — | e-3001 | 7 | no |
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| 19 | Dental hygienist with three years experience, Indianapolis a | e-7086 | 0/1 | — | e-7086 | 0 | no |
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| 20 | Registered nurse with ICU experience, willing to take per-di | w-4936 | 0/1 | — | w-4936 | 0 | no |
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| 21 | Software engineer with React and TypeScript, three years exp | w-334 | 0/1 | — | w-334 | 0 | 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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| 7 | Hazmat-certified warehouse worker comfor | Warehouse worker with Hazmat certification and experience in | e-665 | e-4910 | -1 | no |
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| 13 | Reliable production line lead with stron | Experienced production line supervisor with excellent punctu | e-5778 | w-1950 | -1 | no |
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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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