SlabUploader/docs/PROMPTS.md

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/app/prompts/.

Management

• Location: /app/prompts/ inside the FastAPI container, mounted read-only from host backend/app/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 backend/app/prompts/.

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, wood categories, edge type, figure, grade) is pulled from the synced Woo cache only. No hardcoded mill species list. A term not in the cache cannot be suggested or selected.
• 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.
• Pre-population (when inference_enabled): Call 1 results are the primary source that fills the review screen for species, wood categories, edge type, figure, grade, and mapped feat-* tags from free-text observations.
• Confidence gate (default threshold 0.7):
  • Field confidence ≥ threshold → pre-populate that field (mutable; user may clear or change it).
  • Field confidence < threshold → leave empty. The model's low-confidence guess as a default.
  • Call 1 does not define the ready gate. Species + figure (or any vision subset) is assist/prefill only. Ready is the full named set in AGENTS §7 / DATA-MODEL (filled manually if Call 1 left fields empty). Species + one figure is not enough.
• Manual entry always wins. Call 1 is assist-only and does not gate publish. Full suite must pass with inference OFF. Canonical lock: AGENTS §6–7.

---

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

• Portable Call 1 context (default: full history resend of Call 1 turn + images metadata + assistant JSON). previous_response_id only if test-inference recorded stateful support.
• Explicit confirmed vs inferred labels for species, dimensions, and other attributes (user overrides marked confirmed).
• Deterministic measurements: length, widths, sqft, bdft, thickness (never invented)
• Brand voice, GEO context, title/short templates
• Shared validation module rules so Call 2 assembly rejects the same invalid values the UI would

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. Measurements are not invented; only the provided data is used.

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. 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 {length_in_remainder}in × {width_avg_in}in Wood Slab — {figure} Example description: {llm_prose}. 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}

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.
• Portable Call 1 context (issue #2): default is full resend of Call 1 (user prompt + assistant JSON). Label each taxonomy value confirmed (user-edited) vs inferred (still from Call 1). Stateful previous_response_id is opt-in after test-inference. Fail closed if neither path works.

---

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.