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F9-lite reuses the pre-existing OLLAMA_* config (config.py:36-38: OLLAMA_BASE_URL=https://ollama.com/v1, OLLAMA_API_KEY, OLLAMA_MODEL=kimi-k2.6:cloud). Avoids the local model pull (F9-full would be 4 GB on disk + a separate uvicorn process). Cloud LLM — operator’s existing OLLAMA billing applies per call. Sprint 13 splits the Sprint 11 "Generate Meal Plan" CTA into a 2-step modal: "Use the recipe library" (default, Sprint 11’s existing flow) or "Ask the LLM" (new). The LLM path POSTs to /api/llm/plan with a free-text prompt; the backend calls kimi-k2.6:cloud on ollama.com, parses the LLM’s JSON picks, creates a fresh plan, fills the LLM’s picks, and falls through to the Sprint 6+ fillEmptySlots pattern for the slots the LLM didn’t cover. Backend: - backend/app/api/llm_plan.py (NEW, ~280 lines). 1 endpoint (POST /api/llm/plan body {prompt, week_start}) + 4 helpers: - _ensure_ollama_configured — 503 on missing OLLAMA_API_KEY. - _serialize_library — reads up to 200 recipes for the family, sorted alphabetically. Cap prevents prompt-token overflow on kimi-k2. - _ask_llm — mirrors llm_matcher._ask_ollama (same URL, same headers, max_tokens=800, temperature=0, strips think blocks, 60s timeout). - _parse_picks — tolerant JSON parser. Handles markdown code fences, trailing commentary, and bare JSON. On failure returns []; the library fill takes over. - _validate_picks — drops invalid entries: missing fields, out-of-range day_of_week, unknown meal_type, unknown recipe_id. Returns a list of LLMPickedItem. Flow: rejects duplicate week (400) and empty library (400), builds the prompt, calls the LLM, validates picks, creates the plan, inserts the LLM-picked items, fills the rest from the library (Sprint 6+ pattern, re-implemented inline to avoid a self-HTTP-call), returns {plan_id, picked_count, filled_count, failed_count, reasoning}. - backend/app/schemas/__init__.py — added LLMPlanRequest + LLMPlanResponse. - backend/app/main.py:65-66 — registered llm_plan_api.router at the /api/llm prefix. No collision with the pre-existing WIP recipes.py. Frontend: - frontend/src/api/index.ts — added llm.plan(data) method. - frontend/src/pages/Dashboard.tsx — added the prompt modal (radio for library vs. LLM + textarea for the LLM path with 500-char counter) + new state (showPromptModal, promptMode, promptText, promptBusy) + extracted Sprint 11’s body into generateFromLibrary + added generateFromLLM. The modal is inline (not a separate component) because it depends on 4 local states + 3 handlers. Click-outside-to-dismiss is disabled while promptBusy is true. The textarea autoFocuses when LLM mode is selected. Added the Button import. LLM tolerance: a 60s timeout, parse-failure (markdown code fences, trailing commentary), or empty response all return 0 picks; the library fill takes over. The user never sees a crash — at worst, picked_count: 0 and the toast reads "Planned N meals (LLM picked 0, library filled the rest)". Verified: npm run build green (tsc 0 errors, vite 0 errors). Bundle: 500.28 → 503.82 kB (+3.5 kB). Backend AST clean on all 3 changed files. No new dependencies, no migration, no pre-existing WIP files touched. Deploy: git pull + docker compose up -d --build backend frontend (no migration, no new dependencies).