diff --git a/README.md b/README.md
index d34b1e7..b21758c 100644
--- a/README.md
+++ b/README.md
@@ -19,6 +19,7 @@ Owner: Ty (Whidbey). Store: www.whidbeywoodstore.com. Deploy: .201 (Caddy, Docke
 | `docs/DATA-MODEL.md` | SQLite schema + status state machine |
 | `docs/OPENAPI.md` | Client↔server API contract (deliverable #2) |
 | `docs/CONTENT-WOO.md` | Content templates + WooCommerce payload/integration |
+| `docs/PROMPTS.md` | Call 1 vision prompt + Call 2 content prompt (server files) |
 | `docs/DEPLOYMENT.md` | Compose, Caddy, backup/restore, key rotation (deliverable #4) |
 | `docs/IMPL-PLAN.md` | Phased build plan with acceptance criteria + exit gates |
 | `AGENTS.md` | Working contract + hard rules for anyone implementing |
diff --git a/Slab capture app PRD.txt b/Slab capture app PRD.txt
index 7021cf4..9e361c6 100644
--- a/Slab capture app PRD.txt	
+++ b/Slab capture app PRD.txt	
@@ -20,16 +20,20 @@ publish pipeline.
 1. No calibration photos — user manually enters SKU, length, thickness.
 2. First photo is the measurement photo (must be top-down, lens parallel to broad side).
 3. All photos go through background removal + 80% frame-fill crop.
-4. Output: transparent PNGs, minimum 1600 px on the smaller dimension, aspect ratio preserved.
+4. Output: transparent PNGs, minimum 1600 px on the smaller dimension,
+   cropped to 3:4 portrait (W:H 3:4 = 4:3 portrait) to match the WooCommerce
+   template; configurable in settings.
 5. Green or black sheet — algorithm auto-detects background color from image borders.
 6. WooCommerce categories synced from store: Species (1 required, leaf-only), Wood Category (1+ required, leaf-only).
 7. WooCommerce attributes: Edge Type (1 required), Figure (1+ required), Grade (1+ required), Thickness (1 required, rounded up to nearest Woo attribute range).
-8. WooCommerce tags: fig-* only (1+ required, e.g., fig-chat = Chatoyant).
+8. WooCommerce tags: fig-* and feat-* only (1+ required, e.g., fig-chat, feat-makcr).
+   WooCommerce is the authoritative source; the design does not duplicate tag lists.
 9. Two-call inference: Call 1 (vision, wood expert) → Call 2 (text, content gen). Both configurable.
-10. Call 1 runs automatically after calibration. User reviews/corrects before triggering Call 2.
+10. Call 1 runs automatically after calibration **only if** inference is enabled.
+    User reviews/corrects before triggering Call 2.
 11. Inference timeout >45s or non-200 → error with retry. User can fill fields manually.
 12. Woo product status set to "published" by default (configurable in settings).
-13. All inference prompts stored server-side, editable in settings with reset-to-default.
+13. Inference prompts are server-side text files, read fresh on each invocation.
 14. Brand voice + GEO context injected into Call 2 prompt.
 15. WooCommerce credentials (username + App Password) stored client-side in settings.
 16. Inference endpoint: configurable OpenAI-compatible API (user provides base URL, key, model).
@@ -94,8 +98,8 @@ publish pipeline.
 - **FR3:** App detects background color from image borders (green or black sheet).
 - **FR4:** First photo: BG removal - find longest dimension - compute scale
   (longest_px / user-length) - compute area (sqft) - bdft = sqft * thickness / 12.
-- **FR5:** All photos: BG removal - crop to 80% frame fill - save as transparent PNG,
-  minimum 1600 px on smaller dimension, aspect ratio preserved.
+- **FR5:** All photos: BG removal - crop to 80% frame fill - save as transparent
+  PNG, minimum 1600 px on smaller dimension, 3:4 portrait crop (configurable).
 - **FR6:** User can retake the first photo or abort and start fresh.
 
 ### Dimensions and calculations
@@ -124,22 +128,38 @@ publish pipeline.
 ### Inference (two-call architecture)
 - **FR22:** Call 1 (vision): auto-run after calibration. Input: all inventory photos
   (downscaled to 1024px max, using original pre-crop photos), user metadata, synced
-  Woo taxonomy. Output: species, wood categories, edge type, figure, grade,
-  character/inclusions/voids/checks with severity, top 3 wood categories with
-  confidence. If no species match above threshold, omit and require manual selection.
-- **FR23:** Call 2 (text): user-triggered after Call 1 curation. Input: deterministic
-  data + Call 1 results + brand voice + GEO context. Output: title, description,
-  short description.
-- **FR24:** Inference prompts stored server-side, editable in settings, reset-to-default available.
+  Woo taxonomy. Output: species, wood categories, edge type, figure, grade —
+  selected from Woo taxonomy, each with a confidence score. The model is
+  constrained to choose from the synced Woo taxonomy lists.
+- **FR22a:** Confidence threshold: 0.7. If the model's confidence for any field
+  is below 0.7, the field is left blank and requires manual user selection.
+  Threshold is configurable in settings for tuning.
+- **FR22b:** Species bias: the prompt includes the full Woo species leaf list,
+  constraining the model to select from known species only. This prevents
+  hallucinated species and significantly improves accuracy.
+- **FR22c:** Free-text observations: character, inclusions, voids, and checks
+  are free-text observations. The server maps them to Woo `feat-*` product
+  tags after the call returns. WooCommerce is the authoritative source for
+  feat-* tag definitions; the design does not duplicate them.
+- **FR23:** Call 2 (text): user-triggered. Can use curated Call 1 results OR
+  manually-entered taxonomy. The LLM writes only descriptive prose
+  (character, color, grain, use cases). The server assembles the final title,
+  short description, and full description from deterministic templates with the
+  LLM prose injected. Dimensions are never generated by the LLM.
+- **FR24:** Inference prompts are server-side text files, read fresh on each
+  invocation. A reset-to-default script restores the shipped defaults. Prompts
+  are not user-facing settings; they are tuned by the owner directly.
 - **FR25:** Progress indicator during inference calls (latency expected).
-- **FR26:** Timeout >45s or non-200 - visible error with retry option.
+- **FR26:** Timeout >45s or non-200 - visible error with retry option. Retry is
+  available if the inference endpoint was previously tested as connected.
+  Intermittent failures are expected in low-connectivity environments.
 - **FR27:** All inference results editable; no automatic publish of LLM output.
 - **FR28:** Inference endpoint configured via settings: base URL, API key, model name.
   Test connection verifies text + vision support.
 
 ### Photo normalization and output
 - **FR29:** Background removal: chroma-key for green/black, fallback to U2Net matting.
-- **FR30:** Crop to 80% frame fill, slab centered, aspect ratio preserved,
+- **FR30:** Crop to 80% frame fill, slab centered, 3:4 portrait aspect,
   minimum 1600 px on smaller dimension.
 - **FR31:** Output as transparent PNG (ideal for WooCommerce).
 - **FR32:** Store both original and processed images. Purge after successful publish.
diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index 5be38a2..e9e04d9 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -18,7 +18,7 @@ phone (SvelteKit)
 
 server
   reverse proxy → frontend (static)
-        → fastapi : store, settings, prompts, taxonomy, Woo, inference proxy, U2Net
+        → fastapi : store, settings, taxonomy, Woo, inference proxy, U2Net, prompt files
 ```
 
 - Happy path never leaves the phone.
@@ -77,8 +77,15 @@ flowchart TB
 1. **Happy path** stays on the phone through PNG + numbers. FastAPI is idle until the user has a confirmed mask (or taps Try harder).
 2. **Try harder** is U2Net on **one photo** (the one on screen). Mask comes back; sliders still apply. Not a full re-pipeline on the server.
 3. **Draft upload** sends **originals + processed PNGs + length/thickness/SKU/sqft/bdft/widths**. Originals are needed if Call 1 or a later retry must not depend on the tab still being open. After successful Woo publish, server deletes both.
-4. **Call 1** is server-side so the vision key never sits in the browser. Body: all originals downscaled to 1024 + Woo taxonomy snapshot + SKU/length/thickness. Timeout 45s → error + retry on the phone. User **must** curate before Call 2.
-5. **Call 2** does not auto-fire. User taps Generate text. Server sends curated Call 1 + deterministic numbers + brand/GEO + prompt.
+4. **Call 1** is server-side so the vision key never sits in the browser. Auto-run
+   on slab create **only if** `inference_enabled` is true. If disabled, Call 1
+   is skipped; user can manually fill taxonomy and still trigger Call 2. Body:
+   all originals downscaled to 1024 + Woo taxonomy snapshot + SKU/length/thickness.
+   Timeout 45s → error + retry on the phone. User reviews/corrects results.
+5. **Call 2** does not auto-fire. User taps Generate text. Server sends curated
+   Call 1 results (or manually-entered taxonomy if Call 1 failed) + deterministic
+   numbers + brand/GEO for prose generation; assembles title/description/short
+   description from deterministic templates with the LLM prose injected.
 6. **Woo** is only FastAPI. Browser never sees the App Password. Duplicate SKU → stop, show error, no edit-same-SKU in MVP.
 7. **Settings** (species $/bdft, prompts, endpoint, Woo, publish status) live on the server; the phone is just the form.
 
@@ -90,7 +97,7 @@ flowchart TB
 | Sheet detect, chroma-key / black threshold, sliders, flood-fill | Client |
 | U2Net | Server, on demand |
 | Length axis overlay, 6" widths, sqft, bdft | Client |
-| 3:4 PNG, 80% fill | Client |
+| 3:4 PNG, 80% fill (configurable aspect) | Client |
 | Draft + originals + processed PNGs | Server (after user continues) |
 | Species $/bdft, Woo creds, prompts, brand/GEO | Server |
 | Call 1 (vision, all photos @1024) | Server proxy |
diff --git a/docs/CONTENT-WOO.md b/docs/CONTENT-WOO.md
new file mode 100644
index 0000000..bf9e81b
--- /dev/null
+++ b/docs/CONTENT-WOO.md
@@ -0,0 +1,153 @@
+# Content Generation & WooCommerce Integration
+
+Status: SPEC · 2026-08-24 · SlabUploader
+Covers: FR16 (deterministic content templates + optional LLM refinement), FR17
+(brand voice / GEO), FR18 (editable, never auto-published), FR25–28 (taxonomy sync,
+product create, sync log), and the exact WooCommerce REST v3 payload.
+
+## 1. Content generation (FR16–18)
+
+### 1.1 Deterministic templates (always on)
+Templates are configured in Settings (`content_templates`, editable per FR32).
+Defaults below. Placeholders use `{field}`.
+
+**title** (default):
+```
+{species} {length_ft}ft {length_in_remainder}in × {width_avg_in}in Wood Slab — {character}
+```
+Example: `Black Walnut 16ft 0in × 18.2in Wood Slab — Cathedral`
+- `length_ft` = floor(length_in / 12), `length_in_remainder` = length_in mod 12 (1 dp).
+
+**short_title** (default):
+```
+{species} Slab — {character}
+```
+Example: `Black Walnut Slab — Cathedral`
+
+**description** (default, multi-paragraph):
+```
+A one-of-a-one {character} {species} slab, {length_ft}ft {length_in_remainder}in long with an
+average width of {width_avg_in}in and {thickness_in}in thickness ({bdft} bdft).
+
+Each slab is naturally shaped — width varies along the length at {width_min_in}in to
+{width_max_in}in. Sold by the slab; dimensions as measured. {geo_sentence}
+```
+`geo_sentence` = GEO context sentence from Settings (FR17), e.g.
+`"Locally milled and shipped from the Pacific Northwest."` (default if unset: none).
+
+All template math fields come from the deterministic pipeline (never from the LLM).
+
+### 1.2 Call 2 content generation (toggleable)
+- Triggered only if `content_llm_enabled` is true AND the user taps "Generate text"
+  on the review screen (never automatic).
+- **LLM writes prose only**: the prompt sends the slab's deterministic measurements
+  as facts, and asks the model to write compelling product description prose
+  (character, color, warmth, grain, use cases). Dimensions are never generated
+  by the LLM — they are injected by deterministic templates.
+- **Template assembly**: after the LLM returns, the server assembles the final
+  title, short description, and full description from deterministic templates
+  with the LLM prose injected into the description.
+- No numeric guardrail needed — dimensions never pass through the LLM.
+- Output is always editable (FR18); `content_source` = `llm` when used.
+- If the inference endpoint is text-only or unreachable → the button is disabled,
+  templates still work. No failure mode blocks publish.
+
+### 1.3 Audit
+Every content generation (template or LLM) is logged with inputs (template version,
+slab facts), the LLM request/response payload when used (PRD "inference calls logged"),
+and the final stored content.
+
+## 2. WooCommerce integration (FR24–28)
+
+### 2.1 Auth & client
+- REST v3: `GET/POST/PATCH/DELETE {woo_base_url}/wp-json/wc/v3/…`
+- Basic auth: `woo_consumer_key:woo_consumer_secret` (Decision 10; the "username" is
+  the consumer key, the "app password" is the consumer secret).
+- Client: `httpx` with timeouts (connect 10s, read 60s for image-heavy create).
+- `woo_base_url` default: `https://www.whidbeywoodstore.com`.
+
+### 2.2 Taxonomy sync (FR25)
+`POST /api/admin/taxonomy/sync` pulls, in order:
+1. `GET /products/categories?per_page=100` (paginate) → `woo_taxonomy(kind=category)`
+2. `GET /products/tags?per_page=100` → `kind=tag`
+3. `GET /products/attributes` → `kind=attribute`
+Rebuilds the cache table (delete + insert, single transaction). Returns counts.
+Failure → `502 woo_error`; previous cache retained (never delete-then-fail).
+
+### 2.3 Auto-assign taxonomy (FR26)
+- **Category**: species value is matched to a Woo category by name (case-insensitive,
+  trimmed). Match → that category id. No match → the slab's `category_override`
+  (user picks in review) or empty (Woo required-field validation will surface it).
+- **Tags**: `[character]` plus optional `[species]` if present as a tag.
+  Call 1 feat-* observations are mapped to Woo feat-* tags (e.g., Crack/Check,
+  Resin Ready). WooCommerce is authoritative for tag definitions.
+- **Attributes**: species/character/length/thickness as product attributes if the
+  corresponding Woo attributes exist in the cache (by name). Missing attributes are
+  skipped, not created (v1: no attribute creation).
+- Every assignment is overridable in the review screen (FR26).
+
+### 2.4 Product create payload (FR27)
+`POST /products` — body (only non-null fields sent):
+```jsonc
+{
+  "name": "{title}",
+  "type": "simple",
+  "status": "pending",            // test-target decision: NEVER "publish" automatically
+  "sku": "{sku}",
+  "regular_price": "{price}",     // 2 dp string, e.g. "2730.00"
+  "description": "{description}",
+  "short_description": "{short_title}",
+  "categories": [ { "id": <species category id> } ],
+  "tags": [ { "id": <char tag id> }, … ],
+  "images": [
+    { "src": "<data URL or pre-uploaded attachment URL>", "alt": "{species} {character} slab" }
+  ],
+  "attributes": [ … if present … ],
+  "virtual": false,
+  "downloadable": false,
+  "catalog_visibility": "visible"
+}
+```
+**Image upload strategy** (Woo v3): `images[].src` accepts a URL or a data URL.
+For reliability the server:
+1. For each customer-facing (inventory, normalized) photo in `seq` order:
+   `POST /media` with the JPEG bytes → get attachment `source_url`.
+2. Reference those `source_url` values in `images` (≤5, per PRD 1–5 inventory photos).
+3. Calibration photos are excluded (FR2).
+
+**Status = `pending`** by default (test-target decision: test products must never go
+live without human action). The Settings key `woo_publish_status` (default `pending`)
+controls this; Ty flips it to `publish` only when intentionally going live.
+
+### 2.5 Dedupe & idempotency (FR24a)
+Before create: `GET /products?sku={sku}`.
+- Found → `409 duplicate_sku` (v1 has no update path; human resolves).
+- Not found → proceed. (Single-owner, slab-by-slab, so no TOCTOU concern in practice;
+  the check is still done for correctness.)
+
+### 2.6 Sync result & logging (FR28)
+- On success: set `slabs.woo_product_id`, `status=published`, `published_at`, write
+  `sync_log(status=success, woo_product_id, payload_hash)`.
+- On Woo HTTP error: `status=failed`, `sync_log(status=failed, http_status, error,
+  detail=<Woo error body>)`. Surface in UI + poll response.
+- **Partial-failure note** (risk, see IMPL-PLAN): if media uploads succeed but the
+  final product POST fails, orphaned Woo attachments may exist. v1: log it; a manual
+  cleanup note is surfaced. Not auto-deleted (could delete a real image).
+
+## 3. End-to-end publish sequence (server side)
+```
+1. validate slab publishable (length,width,thickness,price,species,char,≥1 photo)
+2. dedupe by SKU (GET /products?sku=)            → 409 duplicate_sku if exists
+3. (re)sync taxonomy if cache older than 1h       → best-effort; use existing cache on fail
+4. resolve category/tags/attributes (auto-assign + overrides)
+5. upload normalized inventory images → media     → source_urls
+6. POST /products (status=pending)                → woo_product_id
+7. write sync_log(success), set slab published, purge images
+   on any failure at 2–6: write sync_log(failed), slab=failed, (images retained for retry)
+```
+
+## 4. What is deterministic vs scoped-inference here
+- Deterministic: templates, all dimension text, category/tag/attribute resolution,
+  payload assembly, dedupe, logging, title assembly.
+- Scoped inference: optional LLM prose generation for description only. The LLM
+  never touches dimensions.
diff --git a/docs/DATA-MODEL.md b/docs/DATA-MODEL.md
index 5bcb751..fb37423 100644
--- a/docs/DATA-MODEL.md
+++ b/docs/DATA-MODEL.md
@@ -38,6 +38,7 @@ Draft-only. Deleted on successful publish. WooCommerce is the source of truth fo
 | `thickness_term_id` | INTEGER | → woo_attribute_terms (round-up) |
 | `moisture_term_id` | INTEGER | → woo_attribute_terms (1 required, defaults to kiln-dried term; user overrides; not inferred) |
 | `fig_tag_ids` | INTEGER[] JSON | → woo_tags (fig-*, 1+) |
+| `feat_tag_ids` | INTEGER[] JSON | → woo_tags (feat-*, 0+) |
 | `price_per_bdft` | REAL | from pricing_rules for species |
 | `price` | REAL NOT NULL | 2 decimals |
 | `price_source` | TEXT | 'recommendation' \| 'override' |
@@ -158,7 +159,8 @@ The actual selectable values. E.g., "Cathedral" under Figure, "1\"–1 1/2\"" un
 | `slug` | TEXT | |
 | `synced_at` | TEXT | |
 
-Only `fig-*` tags. Other Woo tags ignored.
+Only `fig-*` and `feat-*` tags. Other Woo tags ignored. WooCommerce is the
+authoritative source for tag definitions; this table caches synced Woo tags.
 
 `UNIQUE(woo_id)`.
 
@@ -177,11 +179,15 @@ Keys:
 - `inference_base_url`, `inference_api_key`, `inference_model`
 - `inference_enabled`, `content_llm_enabled`
 - `brand_voice`, `geo_context`
-- `prompts` (JSON: call1_prompt, call2_prompt, reset-to-default)
 - `publish_status` ('published' | 'draft')
 - `aspect_ratio` ('3:4'), `output_px` (1600), `output_px_min` (1600), `fill_target` (0.80)
 - `user_sensitivity`, `user_edge_offset`, `user_feather`, `user_sheet_mode`
 
+**Prompt files** — not in the settings table. Stored as text files in
+`/data/prompts/` (server volume). Read fresh on each invocation. Default
+prompts shipped with the app; owner edits directly. A reset-to-default script
+restores originals.
+
 AES master key is an env var (`SLAB_AES_KEY`, 32 bytes base64). Never in the DB.
 Each `value_enc` = `nonce(12) || ciphertext`.
 
diff --git a/docs/DEPLOYMENT.md b/docs/DEPLOYMENT.md
index d278185..5ea4b40 100644
--- a/docs/DEPLOYMENT.md
+++ b/docs/DEPLOYMENT.md
@@ -42,6 +42,7 @@ SlabUploader/
     │   ├── pipeline/              # deterministic core (constants.py, ruler, geometry, widths, pricing, normalize)
     │   ├── woo/                   # Woo client, taxonomy, publish
     │   ├── inference/             # vision + content wrappers (toggleable)
+    │   ├── prompts/               # call1.txt, call2.txt (shipped defaults)
     │   ├── api/                   # routers (slabs, photos, pricing, taxonomy, settings)
     │   ├── db/                    # schema.sql, models, migrations (alembic)
     │   └── security/              # AES-GCM settings encryption
@@ -67,6 +68,7 @@ services:
     volumes:
       - slab_db:/data
       - slab_images:/images
+      - ./prompts:/app/prompts:ro       # prompt files, read-only mount
     ports: ["127.0.0.1:${FASTAPI_PORT:-18081}:8000"]  # Caddy-only
     mem_limit: 2g                            # env convention: mem_limit preferred
     restart: unless-stopped
@@ -119,8 +121,9 @@ provide `python -m app.security.rekey NEW_KEY` (reads old key from env
 ```
 1. sqlite3 /data/slab.db ".backup /backups/slab-YYYYMMDD.db"   # WAL-safe
 2. tar czf /backups/slab-images-YYYYMMDD.tgz -C /images .
-3. retain: 7 daily + 4 weekly; prune older
-4. log result to /var/log/slab-backup.log; alert (Hermes) on failure
+3. cp -r /backups/prompts-YYYYMMDD.tar.gz prompts/              # prompt files
+4. retain: 7 daily + 4 weekly; prune older
+5. log result to /var/log/slab-backup.log; alert (Hermes) on failure
 ```
 - Backup target: local dir first; mirror to TrueNAS SMB share
   (`\\truenas.local\…\backups\slab`) for off-box safety (same pattern as other stacks).
@@ -138,6 +141,7 @@ provide `python -m app.security.rekey NEW_KEY` (reads old key from env
 | Rekey AES | §5 |
 | Rotate Woo creds | Woo UI → new consumer key/secret → Settings UI → `POST /api/settings/test-woo` |
 | Rotate inference key | Settings UI (if external) |
+| Edit a prompt | Edit the text file on host, no restart needed (read fresh each time) |
 | Rollback image | `git checkout <tag>` → rebuild → up -d; DB down-migrations provided for schema changes |
 | Reset one slab | delete its photos dir + `UPDATE slabs SET status='draft'` (admin script) |
 
diff --git a/docs/IMPL-PLAN.md b/docs/IMPL-PLAN.md
index 2e27d7d..a51d29f 100644
--- a/docs/IMPL-PLAN.md
+++ b/docs/IMPL-PLAN.md
@@ -85,7 +85,7 @@ Deliverables:
   threshold).
 - Publish button (online-only), status polling, success/failure states, error detail.
 - Settings UI (FR32): Woo creds + test, inference endpoint + test (vision check),
-  pricing rules editor, brand voice/GEO, templates.
+  pricing rules editor, brand voice/GEO. Prompts are server files, not UI settings.
 
 Acceptance:
 - Full round-trip in browser: capture→upload→review→edit→publish→published (against
@@ -119,9 +119,12 @@ Goal: scoped, toggleable inference with guardrails.
 
 Deliverables:
 - Vision wrapper: species/character detection from the top-down photo via the
-  configurable OpenAI-compatible endpoint; returns confidence; logs request/response.
-- Content refinement (optional LLM) with the numeric-accuracy guardrail (CONTENT-WOO
-  §1.2).
+  configurable OpenAI-compatible endpoint; model constrained to Woo species list
+  for accuracy; returns confidence (threshold 0.7, configurable); logs
+  request/response.
+- Content wrapper: Call 2 reads prompts/call2.txt; LLM writes prose only,
+  dimensions injected by deterministic templates. No numeric guardrail needed.
+  See docs/PROMPTS.md.
 - `test-inference` endpoint (vision capability check).
 - All inference code behind feature flags; suite passes with everything OFF.
 
@@ -130,8 +133,8 @@ Acceptance:
   confidence; low confidence → manual confirm required.
 - With inference OFF: full app works, tests green (explicit test asserts no network
   calls to the inference endpoint).
-- LLM refinement rejects output that alters a measurement (guardrail test).
 - Inference payloads stored for audit (asserted present after a call).
+- Call 1 prompt file edits survive server restart (read fresh each invocation).
 
 Exit gate: Ty runs species detection on 3 real slabs; results acceptable or manually
 corrected (that's the design).
diff --git a/docs/OPENAPI.md b/docs/OPENAPI.md
index 312efe2..53f8498 100644
--- a/docs/OPENAPI.md
+++ b/docs/OPENAPI.md
@@ -60,6 +60,7 @@ URL is `/api/v1`. Breaking changes bump to `/api/v2`.
   "thickness_term_id": 12,   // → woo_attribute_terms (round-up)
   "moisture_term_id": 5,     // → woo_attribute_terms (defaults kiln-dried)
   "fig_tag_ids": [99],       // JSON array → woo_tags (fig-*, 1+)
+  "feat_tag_ids": [42],      // JSON array → woo_tags (feat-*, 0+)
   "price_per_bdft": 12.50,   // from pricing_rules
   "price": 12.00,            // 2 decimals
   "price_source": "recommendation|override|null",
@@ -86,6 +87,11 @@ All `SlabCreate` fields (resolved) plus:
 ```
 
 ### InferenceCall1Result (returned by infer-taxon)
+
+Taxonomy fields are Woo IDs from the synced cache. Character/inclusions/voids/checks
+are free-text observations; the server maps them to Woo `feat-*` product tags after
+the call returns. WooCommerce is the authoritative source for `feat-*` definitions.
+
 ```jsonc
 { "species_id": 42, "species_confidence": 0.92,
   "wood_category_ids": [15, 23],
@@ -139,7 +145,7 @@ Secrets (keys) are never returned. Only a `*_configured` boolean.
 ### Slabs
 - `POST /api/v1/slabs` — Create a calibrated draft. Body: multipart with `data` (SlabCreate JSON) + `files[]` (photos) + `meta[]` (photo metadata).
   - Returns `201` `Slab` (status `draft`).
-  - Server auto-runs Call 1 (vision inference). `inference_status` becomes `inferring`, then `done`/`failed`.
+  - Server auto-runs Call 1 **only if** `inference_enabled` is true. `inference_status` becomes `inferring`, then `done`/`failed`.
   - `409 duplicate_sku` if SKU already published.
 - `GET /api/v1/slabs` → `200` `[Slab]` (most recent first).
 - `GET /api/v1/slabs/{id}` → `200` `Slab` / `404`.
@@ -159,11 +165,19 @@ Secrets (keys) are never returned. Only a `*_configured` boolean.
 
 ### Inference
 - `POST /api/v1/slabs/{id}/infer-taxon` — Trigger or retry Call 1 (vision).
-  - Server uses cached taxonomy + original photos downscaled to 1024px + user metadata.
+  - Server reads the Call 1 prompt file fresh, uses cached taxonomy + original
+    photos downscaled to 1024px + user metadata, and sends to the configured
+    vision endpoint.
   - Returns `202` `{ "status": "inferring" }`. Client polls `GET /api/v1/slabs/{id}` for results.
   - Timeout > 45s → `504 gateway_timeout` with error detail.
 - `POST /api/v1/slabs/{id}/infer-content` — Trigger Call 2 (text).
-  - Server uses curated Call 1 results + deterministic numbers + brand voice + GEO context + prompt.
+  - Server reads the Call 2 prompt file fresh, uses available taxonomy
+    (from Call 1 results or manually-entered values) + deterministic numbers
+    + brand voice + GEO context, sends to the LLM for prose generation, then
+    assembles title/description/short description from deterministic templates
+    with the LLM prose injected.
+  - Requires that inference was previously tested as connected
+    (`test-inference` succeeded at least once). If not connected, returns `422`.
   - Returns `202` `{ "status": "inferring" }`. Client polls for results.
   - Timeout > 45s → `504` with error detail.
 
diff --git a/docs/PROMPTS.md b/docs/PROMPTS.md
new file mode 100644
index 0000000..ba81a44
--- /dev/null
+++ b/docs/PROMPTS.md
@@ -0,0 +1,178 @@
+# Inference Prompts
+
+Status: SPEC · 2026-08-27 · SlabUploader
+Prompts are server-side text files, not settings values. Read fresh on each
+invocation. Shipped defaults live in `backend/prompts/`.
+
+## Management
+
+- **Location:** `/app/prompts/` inside the FastAPI container, mounted read-only
+  from host `prompts/`.
+- **Editing:** Edit the text files on the host. No server restart needed.
+- **Versioning:** The inference_log stores the prompt content (or hash) with each
+  inference call, so you can trace which prompt version produced which result.
+- **Reset-to-default:** `python -m app.prompts.reset` restores shipped defaults
+  from the app bundle.
+
+## Call 1 — Vision (species/character classification)
+
+File: `/app/prompts/call1.txt`
+
+### Purpose
+Classify the slab's species, edge type, figure, and grade by analyzing
+inventory photos. Constrained to choose from the synced Woo taxonomy lists.
+
+### Inputs assembled by the server
+- 1–5 inventory photos, downscaled to 1024px max
+- Species leaf categories from Woo (list with IDs and brief definitions)
+- Edge Type attribute terms with IDs
+- Figure attribute terms with IDs
+- Grade attribute terms with IDs
+- User metadata: SKU, length, thickness
+
+### System prompt (template)
+```
+You are an expert wood slab classifier. Analyze the provided slab photos and
+return taxonomy data. Only use the values provided in the taxonomy lists — do
+not invent categories or attribute values.
+```
+
+### User prompt (assembled at runtime)
+```
+Analyze the following wood slab photos and classify the species, edge type,
+figure, and grade.
+
+Species options (choose ONLY from this list):
+  1. [species name] — [brief definition] (woo_id: <id>)
+  2. ...
+
+Edge Type options:
+  - Live Edge — natural, uncut bark edge
+  - Flat Sawn — squared, milled edges
+  ...
+
+Figure options:
+  - Cathedral — U-shaped grain patterns
+  - Burl — swirling, knotted grain
+  ...
+
+Grade options:
+  - Premium — minimal defects, clean grain
+  - Standard — some minor characteristics
+  ...
+
+Slab metadata: SKU={sku}, Length={length}in, Thickness={thickness}in
+
+Return valid JSON matching this schema:
+{
+  "species_id": <woo_id or null>,
+  "species_confidence": 0.0-1.0,
+  "wood_category_ids": [<woo_id>, ...],
+  "edge_type_term_id": <woo_id or null>,
+  "figure_term_ids": [<woo_id>, ...],
+  "figure_confidence": 0.0-1.0,
+  "grade_term_ids": [<woo_id>, ...],
+  "grade_confidence": 0.0-1.0,
+  "character": { "type": "string", "severity": "string" },
+  "inclusions": { "type": "string", "severity": "string" },
+  "voids": "string",
+  "checks": "string",
+  "wood_categories": [
+    { "name": "string", "confidence": 0.0-1.0 },
+    ...
+  ]
+}
+```
+
+### Notes
+- The taxonomy list (species, edge type, figure, grade) is pulled from the
+  synced Woo cache each time, so the options always match what's in the store.
+- Brief definitions help the model distinguish ambiguous cases
+  (e.g., "quarter-sawn" vs "cathedral").
+- Character/inclusions/voids/checks are free-text observations. The server
+  maps them to Woo `feat-*` product tags after the call returns. WooCommerce
+  is the authoritative source for feat-* tag definitions; the design does not
+  duplicate them.
+
+---
+
+## Call 2 — Content (description prose)
+
+File: `/app/prompts/call2.txt`
+
+### Purpose
+Generate compelling product description prose for the slab. The model writes
+only the descriptive portion — dimensions, species, and measurements are
+injected by deterministic templates.
+
+### Inputs assembled by the server
+- Curated Call 1 results (species, figure, character, grade)
+- Deterministic measurements: length, widths, sqft, bdft, thickness
+- Brand voice
+- GEO context
+- Title template and short description template
+
+### System prompt (template)
+```
+You are a professional product copywriter for premium, one-of-a-one wood
+slabs. Write compelling, natural-sounding descriptions. Use the brand voice
+provided. Do not invent measurements — use only the provided data.
+```
+
+### User prompt (assembled at runtime)
+```
+Write product copy for this wood slab.
+
+Species: {species}
+Figure: {figure}
+Character: {character description from Call 1}
+Length: {length_ft}ft {length_in_remainder}in
+Average width: {width_avg_in}in
+Width range: {width_min_in}in to {width_max_in}in
+Thickness: {thickness_in}in
+Board feet: {bdft} bdft
+Grade: {grade}
+
+Brand voice: {brand_voice}
+GEO context: {geo_context}
+
+Write:
+1. A compelling opening sentence about the slab
+2. A paragraph describing the character, grain, color, and use cases
+3. A call to action
+
+Keep the tone appropriate for a premium lumber buyer. Do not repeat the
+dimensions — those are handled by the template. Focus on the beauty and
+versatility of the slab.
+
+Return JSON: { "prose": "..." }
+```
+
+### Template assembly (server-side, after LLM returns)
+The server assembles the final title and description from templates with
+deterministic measurements, inserting the LLM prose where appropriate.
+
+Example title: `{species} {length_ft}ft × {width_avg}in Slab — {figure}`
+Example description: `{llm_prose}. Each slab is naturally shaped — width varies
+along the length at {width_min}in to {width_max}in. Sold by the slab;
+dimensions as measured. {geo_sentence}`
+
+### Notes
+- The LLM never touches dimensions. Templates handle all numbers.
+- No numeric-accuracy guardrail needed — dimensions never pass through the LLM.
+- If `content_llm_enabled` is false, the LLM call is skipped and templates
+  are used with a generic description paragraph.
+
+---
+
+## Tuning workflow
+
+1. Deploy the default prompts
+2. Capture a real slab, let Call 1 run
+3. Review the results against your expert judgment
+4. Edit `call1.txt` to improve classification accuracy
+5. Re-run Call 1 via `POST /api/v1/slabs/{id}/infer-taxon` to compare
+6. Iterate until results are consistently accurate
+
+The inference_log stores each iteration's input/output so you can track
+progress across prompt versions.
diff --git a/docs/TECH-SPEC-PIPELINE.md b/docs/TECH-SPEC-PIPELINE.md
index 02bd900..322da72 100644
--- a/docs/TECH-SPEC-PIPELINE.md
+++ b/docs/TECH-SPEC-PIPELINE.md
@@ -115,7 +115,8 @@ Round to 2 decimals. Identity: 1 sqft at 1" thick = 1 bdft.
 
 Applies to every inventory photo of the slab (first photo and the rest).
 
-- Crop aspect: **3:4 portrait** (settings default; matches the current PIP).
+- Crop aspect: **3:4 portrait** (default; matches the WooCommerce template;
+  configurable in settings).
 - Transparent **PNG only**.
 - Slab centered. **80% fill at the extremes** (widest and tallest points of the slab). Extra margin where the slab is narrower.
 - After crop, if the source has enough pixels, shorter side ≥ **1600 px**. If it cannot, warn and ask for a retake — do not invent pixels.
@@ -202,7 +203,7 @@ Price 12.345 → **12.35**.
 4. sqft is the projected top-down area of the mask. Live-edge thickness and underside are not in the area.
 5. bdft = sqft × thickness (inches). Not rounded up to whole board feet.
 6. Sheet is uniform green or black. Odd colors → user picks sheet mode or retakes.
-7. 3:4 is the PIP crop. Changing it later is a settings/code change.
+7. 3:4 portrait is the PIP crop, configurable in settings.
 8. Client does all of this math. Server stores the numbers the client sends.
 
 ## 10. Out of scope here (covered in other docs)