feat: Implement ai_blueprint.md action plan — architectural review & optimisations
Steps 1–7 of the ai_blueprint.md action plan executed:
DOCUMENTATION (Steps 1–3, 6–7):
- docs/current_state_analysis.md: Phase-by-phase cost/quality mapping of existing pipeline
- docs/alternatives_analysis.md: 15 alternative approaches with testable hypotheses
- docs/experiment_design.md: 7 controlled A/B experiment specifications (CPC, HQS, CER metrics)
- ai_blueprint_v2.md: New recommended architecture with cost projections and experiment roadmap
CODE IMPROVEMENTS (Step 4 — Experiments 1–4 implemented):
- story/writer.py: Extract build_persona_info() — persona loaded once per book, not per chapter
- story/writer.py: Adaptive scoring thresholds — SCORE_PASSING scales 6.5→7.5 by chapter position
- story/writer.py: Beat expansion skip — if beats >100 words, skip Director's Treatment expansion
- story/planner.py: validate_outline() — pre-generation gate checks missing beats, continuity, pacing
- story/planner.py: Enrichment field validation — warn on missing title/genre after enrich()
- cli/engine.py: Wire persona cache, outline validation gate, chapter_position threading
Expected savings: ~285K tokens per 30-chapter novel (~7% cost reduction)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Mike Wichers
2100ca2312