Domain Terminology Mapping Prompt Template

This prompt is for enterprise RAG teams that need to translate noisy, natural-language user queries into the precise, controlled vocabulary of an internal knowledge base. The core job-to-be-done is a pre-retrieval query rewrite: taking a user's question like 'what's the policy for remote work?' and mapping it to the canonical terms used in your documents, such as 'Telecommuting Policy' or 'Flexible Work Arrangement Guidelines'. The ideal user is a search engineer, RAG developer, or platform engineer responsible for retrieval quality in a domain where internal taxonomies, product names, or entity codes are the keys to finding the right documents.

Use this prompt when your retrieval system consistently fails on recall because users don't know the 'official' names for things. It is appropriate when you have a well-defined glossary, taxonomy, or entity list to provide as grounding context. The prompt works best as a pre-processing step in a retrieval pipeline, where its output becomes the input to your vector, keyword, or hybrid search. It is not a replacement for fine-tuning a custom embedding model, nor is it a solution for building the taxonomy itself. Do not use this prompt when the domain terminology is unstable, when you lack a canonical source of truth for terms, or when the user's query is already precise enough to retrieve good results without translation.

Before implementing this prompt, ensure you have a maintained glossary or taxonomy document that can be injected into the prompt as [CONTEXT]. The prompt's value is directly proportional to the quality and coverage of that reference data. If your glossary is incomplete, the model will either miss mappings or, worse, fabricate plausible-sounding but incorrect canonical terms. Always pair this prompt with a validation step that checks whether every mapped term actually exists in your provided glossary. For high-stakes domains like healthcare, finance, or legal, require human review of the mapping output before it drives retrieval, and log every mapping decision for auditability. The next section provides the copy-ready template you can adapt to your domain.

This prompt template is the core instruction set you will send to the LLM. It is designed to be copied directly into your prompt management system, IDE, or orchestration layer. The template uses square-bracket placeholders for all dynamic inputs, ensuring you can wire it into a production RAG pipeline where user queries, domain taxonomies, and output schemas vary per request. Before using this template, ensure you have a validated glossary, taxonomy, or entity list ready to insert into the [DOMAIN_GLOSSARY] placeholder. The prompt instructs the model to act as a strict translator, not a knowledge base, which reduces the risk of hallucinated terminology.

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You are a domain terminology mapper for an enterprise retrieval system. Your only job is to translate user queries into the controlled vocabulary provided below. You must not introduce terms, entities, or identifiers that are not explicitly present in the provided glossary or taxonomy.

## INPUT
User Query: [USER_QUERY]

## DOMAIN GLOSSARY / TAXONOMY
[DOMAIN_GLOSSARY]

## CONSTRAINTS
[CONSTRAINTS]

## OUTPUT SCHEMA
You must return a valid JSON object conforming to this schema:
[OUTPUT_SCHEMA]

## INSTRUCTIONS
1. Analyze the user query and identify all terms, phrases, or entity references that can be mapped to the provided glossary.
2. For each identified term, find the single best canonical match in the glossary. If multiple candidates exist, select the one with the highest context fit and flag it as ambiguous.
3. If a term has no match in the glossary, include it in an 'unmapped_terms' list with a suggested action (e.g., 'add_to_glossary', 'ask_user_for_clarification').
4. Assign a confidence score (0.0 to 1.0) to each mapping. Use 1.0 only for exact string matches. Use lower scores for semantic matches, abbreviation expansions, or context-dependent mappings.
5. If the overall mapping confidence is below the threshold specified in [CONSTRAINTS], set the 'requires_review' flag to true.
6. Do not fabricate IDs, codes, or canonical terms. If the glossary does not contain a required mapping, you must report it as unmapped.

## EXAMPLES
[EXAMPLES]

After copying this template, you must replace each placeholder with concrete values for your domain. The [DOMAIN_GLOSSARY] should be a structured JSON object or a delimited text block containing canonical terms, their synonyms, and any associated identifiers. The [OUTPUT_SCHEMA] must be a valid JSON Schema that your application parser expects, including fields for mapped terms, confidence scores, and unmapped terms. The [CONSTRAINTS] placeholder should specify rules like minimum confidence thresholds, maximum allowed unmapped terms, or required fields. The [EXAMPLES] placeholder should contain 2-4 few-shot examples showing the expected input-output pairs. If you are operating in a regulated domain, add a constraint requiring the model to refuse mapping when confidence is below a strict threshold and escalate for human review. Never deploy this prompt without first running it against a golden dataset of known query-to-terminology pairs to measure mapping accuracy and hallucination rate.

The Domain Terminology Mapping prompt is not a standalone chatbot interaction—it is a pre-retrieval transformation step that must execute reliably before any search query hits your vector store, keyword index, or hybrid retrieval backend. In a production RAG system, this prompt sits between the user's raw input and the retrieval engine. Its job is to translate colloquial, ambiguous, or non-standard user language into the controlled vocabulary, canonical entity names, and internal identifiers that your knowledge base actually uses. Without this translation layer, even the best embedding models and retrieval strategies will fail to surface relevant documents because the user's words simply do not match the indexed terms.

To wire this prompt into an application, wrap it in a function that accepts the user query, a domain glossary or taxonomy (as a structured JSON object or a pre-loaded reference string), and optional configuration parameters such as the maximum number of mappings to return, a confidence threshold, and a flag for whether to include source attribution. The function should construct the prompt by injecting these inputs into the [USER_QUERY], [DOMAIN_GLOSSARY], and [CONSTRAINTS] placeholders. After calling the LLM, parse the output against a strict JSON schema that expects an array of mapping objects, each containing user_term, canonical_term, confidence (a float between 0.0 and 1.0), and an optional source field. Reject any response that does not conform to this schema and retry with a repair prompt that includes the validation error. Log every mapping attempt—including the raw user query, the glossary version used, the model response, and the validation result—so that terminology gaps and hallucinated mappings can be audited later.

Model choice matters here. This prompt requires strong instruction-following and structured output discipline, not creative generation. Use a model that supports strict JSON mode or function calling (such as GPT-4o with response_format, Claude 3.5 Sonnet with tool use, or a fine-tuned open-weight model trained for schema-adherent extraction). Avoid models that tend to paraphrase or embellish, because a fabricated canonical term will silently poison downstream retrieval. Set the temperature low (0.0–0.2) to maximize deterministic behavior. If your glossary is large—thousands of terms—do not paste the entire glossary into every prompt. Instead, use a retrieval step to pre-select the top-N candidate terms from the glossary based on embedding similarity to the user query, then pass only those candidates into the mapping prompt. This keeps token usage manageable and reduces the risk of the model selecting a term outside the provided candidates.

Build a validation layer around the prompt output before any mapped terms reach the retrieval engine. Check that every canonical_term exists in the provided glossary or taxonomy. If a term is not found, flag it as a potential hallucination and either discard it, fall back to the original user term, or route the query to a human review queue if the domain is high-risk (healthcare, legal, finance). Implement a confidence threshold filter: mappings below a configurable cutoff (e.g., 0.7) should be treated as uncertain and either omitted or surfaced to the user for confirmation. For enterprise deployments, maintain a versioned glossary artifact and include the glossary version in every log entry so that mapping failures can be correlated with glossary changes. Finally, expose a dashboard or alert that tracks the rate of unmapped terms, low-confidence mappings, and schema validation failures—these metrics are your leading indicators that the glossary needs updating or the prompt needs tuning.

When integrating this prompt into a retrieval pipeline, decide whether the mapped terms replace the original query, augment it, or both. A common pattern is to generate a rewritten query string that substitutes canonical terms for user terms, then issue that rewritten query to the retrieval backend while also logging the original query for traceability. In hybrid search systems, you may want to issue multiple query variants: one with the original terms (for high-recall vector search) and one with the canonical terms (for high-precision keyword search). The prompt output should include enough metadata to support both paths. Avoid the temptation to skip this mapping step because 'the embeddings will handle it'—embeddings are not immune to terminology gaps, and controlled vocabulary mapping consistently improves retrieval precision in enterprise domains where internal jargon, product codes, and legacy naming conventions dominate the document corpus.