Personalization Engine Local Production-Readiness Spine
From AI anxiety to AI fluency: assessment, simulation, personalized curriculum, and proof.
The intended production object, proven here only in local/headless fixtures, is the AIFluencyCapabilityMap. Titles, workflows, tools, evaluator scores, proof refs, embeddings, and source authority all feed the same future loop: learner and role context becomes a capability map, simulation, evaluation, proof, and the next curriculum move.
Make AI Fluency Verifiable
The mission is to turn the VDS-local Personalization Engine into a self-contained local production-readiness spine for AI fluency, not just a large title-routing table.
Market grounding supports the wedge: Skillsoft names the leader/employee readiness gap and weak formal assessment coverage; Unite.AI frames capability mapping; GCheck frames the verification vacuum around overstated AI skills.
The New Spine Has A Passing P0 Receipt
The 95ebcca Add clean contextual Gemini embedding lane checkpoint remains the preservation baseline. The current local checkpoint extends that base with named workplace tool authority, a small live Gemini embedding slice, a named-tool AI Fluency coverage join, a JD-source tool-context join, public O*NET repairs, and a raw LinkedIn JD repair lane. Ambiguous titles are no longer treated as title-only repair problems when the source row has JD, company, industry, seniority, responsibilities, tools, source refs, and proof context.
| June 13 surface | Proof |
|---|---|
| Preservation | VDS bundle verifies at /srv/aina/checkpoints/aina-data-engine-room/2026-06-13-production-semantic-spine-m5/aina-data-engine-room-95ebcca.bundle; receipt confirms linkedin_jobs=129165. |
| Chunk reconciliation | base_chunk_count=294671, combined_chunk_count=322515, vector_row_count=6506, stale_vector_count=0. |
| Clean contextual embeddings | jd_aware_role_context=288 vectors and ai_fluency_headless_loop=48 vectors are present; 50-row semantic QA passed with 0 failures and 0 raw JD key hits. |
| Tool-category fallback | 83 repaired generic tool-category chunks now have vectors: jobs_research_tool=73, workflow_tool_evidence=10. These are not named-tool authority for Workday/Salesforce/SAP/Dayforce. |
| Named-tool authority | named_tool_authority=20 vectors are present. The receipt scans the 26,813-row donor tool registry and related audits but promotes only the target authority surface; Oracle stays blocked as too broad and 3 suspicious skill-only signals are blocked. |
| Tool-context coverage | Top-band AI Fluency coverage now joins source-backed workflow, named-tool, and JD-source tool context without claiming learner proficiency: top 1,000 has 1,000 any-tool-context rows, including 509 workflow-tool rows, 491 JD-source rows, and 121 named-tool authority rows. |
| Non-tool capability coverage | The proof-tail fixture lane now preserves cumulative fixtures and uses strong JD/workflow/source-authority context refs when title source-ref counts are stale or narrow. Top 1,000 now has 998 local judgment-rubric/proof proxies and 1,000 data-boundary policy proxies; top 500 now has 500 rubric/proof proxies and 500 data-boundary proxies. |
| Proof-tail authority repair | artifacts/validation/ai_fluency_proof_tail_authority_repair_v1.json now has 18 cumulative ready repairs and 2 still blocked rows. Raw LinkedIn JD evidence repairs teacher-science, teacher-social studies, teacher-computer, court judicial assistant in Teller County, landscape designer, AML/SIU SME, and food/beverage lead auditor. Lab assistant and ecommerce manager remain blocked as context-only or too broad. It allows 0 embeddings, creates 0 batch candidates, mutates 0 donor repos, and unlocks 0 runtime authority. |
| Tool-context hardening queue | The previous 493 top-band tool-context gaps are now resolved by source context. The hardening receipt has tool_context_gap_queue_row_count=0, top_500_tool_context_gap_row_count=0, embedding_allowed_count=0, and batch_candidate_count=0. |
| Retrieval boundary | AIN-510 is promotion_ready; exact-cosine remains source-of-truth; runtime embedding authority remains false. |
| Production boundary | Public runtime, real-user data, external writes, and production telemetry remain false. |
Important caveat: this is not full-corpus vectorization. Current coverage is 6506 / 322515, leaving 316009 chunks unvectorized. The supported claim is that the contextual JD-aware, AI Fluency loop, repaired generic tool-category, and named-tool authority slices are cleanly embedded and AIN-510 is promotion-ready locally. Local telemetry artifacts may exist; external writes and production telemetry are not enabled.
| Surface | Current proof |
|---|---|
| AI Fluency P0 | artifacts/validation/ai_fluency_capability_map_v0.json passes with 5 layers, 5 capabilities, 3 workflow requirements, 5 observations, 1 proof ref, 5 suppressed heatmap rows, overall_score=0.858, and no failed checks. |
| AI Fluency top-band coverage | artifacts/validation/ai_fluency_top_band_capability_coverage_v1.json passes with 1,000 rows, 500 hardening rows, complete top 1,000/top 500 vectors, 1,000 any-tool-context rows, 488 JD-source tool-context rows, 121 named-tool authority joins, 998 local judgment-rubric/proof proxies, and 1,000 data-boundary policy proxies. The remaining top-1,000 hardening gap is 2 deliberately blocked proof-tail rows plus learner-observed evidence. |
| AI Fluency proof-tail fixtures | artifacts/validation/ai_fluency_proof_tail_fixtures_v1.json passes with 45 source-backed local fixtures, 2 blocked queue rows, 0 embedding candidates, 0 batch candidates, 0 production unlocks, and no live Gemini calls. |
| AI Fluency proof-tail authority repair | artifacts/validation/ai_fluency_proof_tail_authority_repair_v1.json passes with 20 input blocked rows, 18 ready repairs, 2 still blocked rows, 7 raw LinkedIn JD repairs, 0 embedding candidates, 0 batch candidates, 0 production unlocks, no donor repo mutation, and no live Gemini calls. |
| AI Fluency tool-context hardening | artifacts/validation/ai_fluency_tool_context_hardening_v1.json passes with status tool_context_gaps_resolved_by_source_context, 0 queue rows, 0 top-500 gaps, 0 embedding candidates, 0 batch candidates, 0 title-only inference, 0 learner proficiency claims, and 0 production unlocks. |
| Full validation | artifacts/validation/full_validation.json has status: pass. |
| Warehouse | 110,184 occupation/suitability rows; 129,165 LinkedIn rows; 47,837 wedge rows. |
| HF sources | 15 selected files verified; 186,743,670 bytes processed; 220 GDPval tasks; 907 mapped SOC entries. |
| Public sources | O*NET 30.3 has 1,016 occupation rows and 18,796 task rows; BLS cache has 95 SOC rows in the current source-authority receipt. |
| Top worked titles | Top 1,000 serviceable ICP titles: 29 serve-now and 971 fallback; 0 production unlocks. They are now an input to capability coverage. |
| Runtime | 23 golden cases; 17 local synthetic plans; 17 evaluator passes; public/runtime/real-user/telemetry unlocks all zero. |
| AIN-506/AIN-510 | The June 13 clean contextual and named-tool embedding runs used Vertex ADC project aina-495702; future live Gemini jobs still require the paid-project/privacy/spend gate. AIN-510 is promotion-ready locally, with runtime authority still not promoted. |
What Production-Ready Means
| Domain | Production-ready means |
|---|---|
| Capability map | Every served learner/role can produce or explicitly gap task exposure, tool proficiency, judgment quality, data discipline, and outcome evidence. |
| Source authority | Every runtime decision points to versioned source refs and source eligibility rules. |
| Title routing | The top 1,000 ICP job titles are routed safely, with top 500 hardened first if needed, and unsupported titles refused. |
| Runtime behavior | The engine produces modules, exercises, rubrics, evaluator results, proof refs, and next recommendations across production cohorts. |
| Embeddings | Gemini helps map or retrieve only after source-family quality, exact-cosine quality, and AIN-510 promotion gates pass. |
| Enterprise heatmap | Aggregate readiness rows suppress small cells and never expose learner-level drilldown. |
| Boundary | UI, auth, public dashboards, badges, employer portals, real learner data, and production telemetry remain later integration layers. |
The Path To Production
M0 - Reconcile Current State
Close or explicitly park workflow-repair and embedding lanes; prove artifact policy; keep legacy review-gate fields out of new AI Fluency artifacts.
M1 - Capability Source Authority
Inventory capability assets from donor repos, founding docs, jobs research, Evidence Atlas, HF/GDPval/O*NET/BLS, PKM/Wiki/Daily, and Linear; classify each source by trust and next action.
M2 - AI Fluency Capability Map Contract
Define and prove AIFluencyCapability, WorkflowCapabilityRequirement, AIFluencyCapabilityMap, CapabilityObservation, ProofArtifactIndex, and EnterpriseFluencyHeatmapRow.
M3 - Role, Task, Tool, Risk, And Proof Joins
Join top 500/top 1,000 ICP titles to workflow exposure, capability requirements, tool context, risk tags, evaluator rubrics, data-boundary policy proxies, and proof-ref readiness. Source-backed workflow, JD, and named-tool context now produce 1,000/1,000 top-band any-tool-context coverage without claiming learner proficiency. Existing evaluator, repaired chunk-title, source-authority repair, raw JD evidence, risk-policy assets, and cumulative proof-tail fixtures now add 998 local judgment-rubric/proof proxies and 1,000 data-boundary policy proxies without claiming learner proof; 2 blocked proof-tail rows remain repair-first.
M4 - Assessment, Simulation, Evaluator, Proof
Make the headless loop pass: assessment emits the map, sandbox exercises capability layers, submit creates dimension scores, and proof artifacts store refs/hashes only.
M5 - Clean, Repair, Embed Capability Sources
Done for three small slices: 336 JD-aware and AI Fluency loop chunks have current vectors, 83 repaired generic tool-category fallback chunks were embedded, and 20 named-tool authority chunks were embedded with 0 failed rows. Larger families still follow the progressive ladder.
M6 - Retrieval And Personalization Runtime
AIN-510 is promotion-ready for local exact-cosine retrieval, with 6,506 matched vectors, top 500/top 1,000 vector coverage complete, sensitive buckets present, rollback proof recorded, and runtime embedding authority still false.
M7 - Enterprise Heatmap And Production Boundary
Generate aggregate heatmap rows without learner drilldown, keep public runtime and production telemetry blocked, and prepare archive-first donor retirement proof without deleting any repo.
Issue Shape
Linear document: cd283205-89be-457a-a482-dad368143147.
| Issue | Purpose |
|---|---|
| AIN-507 | PE Production Readiness Board. |
| AIN-508 | M1: capability source authority. |
| AIN-509 | M2: AI Fluency Capability Map contract. |
| AIN-510 | M5/M6: embeddings and retrieval promotion. |
| AIN-511 | M4: assessment/simulation/evaluator/proof loop. |
| AIN-512 | M3: top-cohort role/task/tool/risk joins. |
| AIN-513 | M7: enterprise heatmap and operations boundary. |
| AIN-514 | Platform integration and release boundary. |
Repair The Remaining Proof-Tail Queue
The next autonomous run should resolve the remaining 2 blocked top-band AI Fluency proof-tail rows by using source-backed JD, company, industry, responsibility, workflow, and source-authority evidence where it is strong enough, while keeping lab assistant and ecommerce manager blocked unless they can be disambiguated without title-only guessing. The tool-context loop is closed, data-boundary coverage is complete for the top 1,000, and 45 proof-tail fixtures are now safely source-backed.
cd /srv/aina/aina-data-engine-room
git status --short --branch
uv run aina-data-engine --root /srv/aina/aina-data-engine-room ain-506-p0-gate
uv run aina-data-engine --root /srv/aina/aina-data-engine-room ain-510-retrieval-promotion-gate
uv run aina-data-engine --root /srv/aina/aina-data-engine-room ai-fluency-proof-tail-authority-repair
uv run aina-data-engine --root /srv/aina/aina-data-engine-room ai-fluency-proof-tail-fixtures
uv run aina-data-engine --root /srv/aina/aina-data-engine-room ai-fluency-capability-coverage
uv run aina-data-engine --root /srv/aina/aina-data-engine-room ai-fluency-tool-context-hardening
uv run aina-data-engine --root /srv/aina/aina-data-engine-room production-runtime-readiness
uv run aina-data-engine --root /srv/aina/aina-data-engine-room validate
jq '{status, tables, source_authority_registry_v2_summary, production_chunk_vector_reconciliation_summary}' artifacts/validation/full_validation.json
The repo is much closer to a self-contained local data authority for the contextual spine; the next useful work is scaling proven-clean families while keeping public/runtime unlocks deliberately off.