ai-tuning-loop

AI Tuning Loop

Goal: raise chat answer quality for one agent + category per iteration. Cadence: per agent / category.

Phase 0 · IDEA

• Find the gap using vc-evals-auditor for a target agent and category.
• verify: a named gap with concrete examples (not just "make it better").

Phase 1 · PLAN

• Write docs/features/chat/training/<run-name>.md.
• Detail the gap, current score, target score, batch size.
• Hard guardrails: NO hard-coded scores, NO fabricated source_model, NO duplicate Q&A, mandatory PII scrubbing.
• verify: plan cites gap, target metric, and guardrails.

Phase 2 · IMPLEMENT

• Run real generator and evaluator scripts (e.g. financial-qa:generate, financial-qa:evaluate).
• Avoid running massive parallel batches locally to prevent OOM.
• verify: batch generated, every item has a computed score.

Phase 3 · TEST

• Validate schema (financial-qa:validate).
• Import and register staging data into DB (financial-qa:import, financial-qa:register).
• verify: 0 validation errors; rows registered; deduplication respected.

Phase 4 · QUALITY GATE

• Run the evals (eval:financial-qa).
• Re-run vc-evals-auditor from Phase 0.
• Check security / prompt injections.
• verify: target category score improved; guardrails intact.

Phase 5 · SHIP

• Promote production sessions to corpus (holo:promote).
• Create PR: "feat(ai): tune / + eval".
• verify: CI green; corpus delta reviewed.

Phase 6 · REFLECT

• Append ITERATION_LOG.md: eval delta, cost, near-misses.
• Queue next weakest category for Phase 0.
• Reconcile local state with DB.
• verify: gap closed, next category queued, state reconciled.