Hierarchical Category Extraction Prompt Template

Use this prompt when your platform relies on a nested taxonomy—such as product catalogs, content management systems, or enterprise knowledge bases—and you need to map free-text user queries to fully qualified category paths. The job-to-be-done is translating a natural language request like 'women's trail running shoes' into a structured, hierarchical path such as Footwear > Athletic > Running > Trail > Women's. This is essential for faceted search, content routing, and analytics where flat keyword matching fails to capture parent-child relationships. The ideal user is a search engineer, RAG system builder, or platform developer who controls a taxonomy and needs to bridge the gap between unstructured user language and structured filter clauses.

This prompt is not a general-purpose classifier. Do not use it when your categories are flat, when the taxonomy is unknown or unstable, or when the user's intent is better served by keyword expansion or vector similarity alone. It is also the wrong tool for extracting arbitrary metadata constraints like price ranges or date filters—those require dedicated extraction prompts. The prompt assumes you have a defined taxonomy version and can supply it as context. If your taxonomy changes frequently, you must implement a version alignment check in your harness to prevent the model from generating paths that no longer exist. For high-stakes applications like medical coding or legal document classification, this prompt is a starting point; you must add human review, confidence thresholds, and audit logging before production use.

Before implementing, confirm that your retrieval system can consume hierarchical paths as filter clauses. If your search backend only supports flat tags, you may need to flatten the extracted paths or use them for routing rather than direct filtering. The next section provides the copy-ready prompt template. Adapt the taxonomy placeholder to match your actual category tree, and always test with partial-path queries—users rarely specify the full hierarchy, and the model must handle missing levels gracefully.

This template maps a user's natural language query to one or more hierarchical category paths within your defined taxonomy. It is designed to be wired into a RAG pre-retrieval step, a search filter builder, or a product routing system. The prompt enforces parent-child relationship integrity, handles partial path matching, and explicitly separates high-confidence matches from speculative ones. Use it when your platform relies on a nested taxonomy (e.g., Electronics > Computers > Laptops) and user queries must be translated into structured category constraints before retrieval or product display.

textCopy
You are a taxonomy mapping assistant. Your job is to map a user's natural language query to one or more valid hierarchical category paths from the provided taxonomy. Follow these rules strictly.

TAXONOMY (version: [TAXONOMY_VERSION]):
[TAXONOMY_TREE]

USER QUERY:
[USER_QUERY]

INSTRUCTIONS:
1. Identify the most specific category path(s) that match the user's intent. A path is a sequence from root to leaf, e.g., "Electronics > Computers > Laptops".
2. If the query matches a parent category but not a specific leaf, return the parent path and set `is_partial_match` to true.
3. If the query could match multiple distinct paths, return all plausible matches ranked by confidence.
4. If no category matches, return an empty list.
5. Do not invent categories outside the provided taxonomy.
6. For each match, provide a `confidence` score between 0.0 and 1.0.
7. If the query uses synonyms or non-canonical terms, map them to the closest canonical category name and note the mapping in `term_mapping`.

OUTPUT SCHEMA (JSON only, no markdown):
{
  "matches": [
    {
      "path": "string (e.g., 'Electronics > Computers > Laptops')",
      "confidence": 0.0-1.0,
      "is_partial_match": true/false,
      "matched_terms": ["term_from_query"],
      "term_mapping": {"query_term": "canonical_term"}
    }
  ],
  "ambiguity_flag": true/false,
  "ambiguity_note": "string explaining ambiguity if flag is true"
}

[CONSTRAINTS]

Adaptation guidance: Replace [TAXONOMY_VERSION] with a version string or date so you can detect taxonomy drift in logs. [TAXONOMY_TREE] should be a structured representation of your category hierarchy—indented text, a JSON tree, or a bulleted list all work, but be consistent. [USER_QUERY] is the raw input from your search or chat interface. The [CONSTRAINTS] placeholder is for domain-specific rules: for example, "queries mentioning 'gaming' must prefer the Gaming subcategory over general-purpose equivalents" or "if confidence is below 0.6, flag for human review." If your taxonomy is large, consider injecting only the top two levels and letting the model request deeper branches via a tool call rather than packing the entire tree into the prompt.

Production hardening: Before deploying, add a post-processing validator that checks every returned path against your canonical taxonomy list. A model can hallucinate a plausible-sounding path like Electronics > Computers > Tablets > Gaming Tablets even if Gaming Tablets doesn't exist in your tree. Reject any path that fails this membership check and either retry with a stricter prompt or fall back to a broader parent category. For high-stakes routing (e.g., compliance-sensitive content, pricing tiers), route low-confidence matches (confidence < [THRESHOLD]) to a human review queue. Log every extraction with the taxonomy version, query, extracted paths, and confidence scores so you can measure drift and accuracy over time.

Integrating the hierarchical category extraction prompt into a production system requires more than a single API call. The prompt must be embedded in a harness that validates taxonomy alignment, handles partial path matches, and caches results to avoid redundant extraction for repeated queries. This section covers the application-layer components you need to build around the prompt: input preprocessing, output validation against your taxonomy version, retry logic for malformed paths, and logging for taxonomy drift detection. The goal is to make the extraction reliable enough to drive faceted search filters, content routing, or analytics pipelines without silently producing broken category paths.

Start by loading your taxonomy as a structured schema—typically a JSON tree with id, label, children, and version fields. Before calling the LLM, inject the taxonomy version and a pruned subset of relevant top-level categories into the prompt's [TAXONOMY_CONTEXT] placeholder. After receiving the model output, validate each extracted path against the live taxonomy: check that every node in the path exists, that parent-child relationships are correct, and that the path terminates at a valid leaf or intermediate node as your use case requires. For partial matches—where the model returns a path that is mostly correct but uses a deprecated label or misses a level—implement a fuzzy resolver that finds the closest valid path using edit distance on labels or taxonomy graph traversal. Log all mismatches with the taxonomy version and query text so you can detect when your taxonomy has drifted and the prompt needs updating.

For production reliability, wrap the extraction call in a retry loop with exponential backoff. Common failure modes include the model returning a flat list instead of a hierarchical path, inventing categories not in the taxonomy, or producing paths with incorrect parent-child ordering. Your validator should detect each of these and trigger a retry with a more constrained prompt variant that includes explicit path format examples and a stricter [CONSTRAINTS] block. If retries exhaust, fall back to a conservative default category or escalate to a human review queue. Cache extraction results keyed by a hash of the query text and taxonomy version to avoid redundant LLM calls for repeated or similar queries. Finally, emit structured logs with the query, extracted path, taxonomy version, confidence score, and validation status so you can monitor extraction quality over time and trigger taxonomy alignment reviews when failure rates spike.