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- Introduced a roadmap-first approach for retrieval, allowing for structured exercise suggestions based on stage specifications and major steps. - Added functionality to generate gap-fill offers for unfilled roadmap stages, improving the relevance of exercise recommendations. - Updated the `ExerciseProgressionPathBuilder` to support the new roadmap-first feature, enhancing user experience with clearer exercise paths. - Incremented application version to 0.8.206 and updated the database schema version to reflect these changes.
88 lines
3.5 KiB
Markdown
88 lines
3.5 KiB
Markdown
# Planungs-KI — Produkt-Roadmap
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**Stand:** 2026-06-07
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**App-Version:** ab **0.8.204** — maßgeblich `backend/version.py`
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Diese Roadmap ergänzt die **Architektur-Refaktor-Roadmap** (`UMSETZUNGSPLAN_ROADMAP.md`) und gilt **nur für KI-gestützte Trainingsplanungsunterstützung**.
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**Leit-Spec:** `.claude/docs/working/PLANNING_PROGRESSION_ROADMAP_SPEC.md`
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---
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## Strategische Entscheidung (verbindlich)
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1. **Progressionsgraph:** Planung **vom Ziel rückwärts** (Roadmap-first), nicht Bibliothek-first.
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2. **Keine Gruppenanalyse** im Graphen — Kontext = Zieltext, Thema, Schrittanzahl, optional Graph-Kanten.
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3. **Trainingsplanung** (Einheit, Rahmen, Abschnitt): eigene Pipeline später, **mit** Gruppenkontext — siehe `AI_PLANNING_KI_MULTISTAGE_FORECAST.md` S0–S4.
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4. **Orchestrierung:** Workflow-**lite** jetzt (`planning_progression_roadmap.py`); Mitai Workflow-Engine **später**, wenn 2–3 Pipelines stabil sind.
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---
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## Phasen-Übersicht
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| Phase | Domäne | Kurzbeschreibung | Status |
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|-------|--------|------------------|--------|
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| P0–P2 | Übungssuche | Kontext-Pack, Hybrid-Score, LLM-Rerank | ✅ |
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| A–C2 | Übungssuche | Voll-Library, Graph, Varianten | ✅ |
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| C3 | Progressionsgraph | Pfad-Builder (retrieval-first) | ✅ |
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| E–E3 | Progressionsgraph | Semantik, QA, Lücken-Angebote | ✅ |
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| **F0–F1** | Progressionsgraph | Roadmap-Pipeline Scaffold + API-Preview | 🔄 **0.8.204** |
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| **F2–F4** | Progressionsgraph | LLM Roadmap, roadmap-first Retrieval, UI Review | 🔲 |
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| D | Übungs-Neuanlage | `planning_context` an `suggestExerciseAi` | 🔲 |
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| G | Trainingsplanung | Kontext-Pack Gruppe/Historie, S0–S4 | 🔲 |
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| H | Plattform | Mitai-Workflow-Engine (optional) | 🔲 Backlog |
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---
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## Phase F — Progressions-Roadmap (aktiver Fokus)
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### F0 — Foundation (0.8.204)
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- [x] Spec `PLANNING_PROGRESSION_ROADMAP_SPEC.md`
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- [x] Modul `planning_progression_roadmap.py` (Pydantic, Pipeline-Skeleton)
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- [x] Migration **078** Prompt-Slugs (Zielanalyse, Roadmap)
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- [x] API: `include_roadmap_preview` auf `progression-path-suggest`
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- [x] Doku: HANDOVER, PLANNING_EXERCISE_SUGGEST_CONTEXT, MULTISTAGE_FORECAST
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### F1 — Deterministische Roadmap
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- [x] Phase A aus Semantic Brief
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- [x] Phase B: `micro_objectives` aus `development_arc` + Konsolidierung auf N
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- [x] Phase C: heuristische `stage_specs`
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- [ ] pytest für Konsolidierung
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### F2 — LLM Roadmap (0.8.205)
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- [x] Prompts **078/079** in `ai_prompts` — Code nur Slugs (`PROMPT_SLUG_*`)
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- [x] `include_llm_roadmap` + `load_and_render_ai_prompt` + JSON-Validierung
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- [x] Deterministischer Fallback wenn Prompt/OpenRouter fehlt
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- [ ] Response/UI: genutzte `prompt_slugs` sichtbar machen (Admin-Hinweis)
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### F3 — roadmap-first (0.8.206)
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- [x] Retrieval pro `major_step` + `stage_spec` statt iterativem Pfad-Bau
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- [x] Gap-Angebote für unbesetzte Roadmap-Stufen (`roadmap_unfilled`)
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- [ ] QA/Lücken vollständig an Roadmap koppeln (Brücken optional reduzieren)
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### F4 — UI
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- [ ] Roadmap-Review im `ExerciseProgressionPathBuilder`
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- [ ] Major Steps editierbar vor Übungs-Match
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---
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## Abhängigkeiten
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| Von | Nach | Hinweis |
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|-----|------|---------|
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| F2 | Enrichment / Skills | Bessere Roadmap bei technikspezifischen Skills |
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| F3 | F2 | LLM-Roadmap oder stabile heuristische B |
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| G | F4 | Trainingsplanung kann Roadmap aus Graph referenzieren |
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| H | G + F4 | Workflow-Engine lohnt bei verzweigten Planungsflows |
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---
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## Pflege
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Bei Abschluss einer Teilphase: diese Datei, `HANDOVER.md` §2.8, `PLANNING_EXERCISE_SUGGEST_CONTEXT.md` §24, Changelog in `version.py`.
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