Metadata Constraint Parsing Prompt Template

This prompt is for RAG system builders and search engineers who need to translate unstructured natural language queries into structured, typed metadata filter clauses. The job-to-be-done is bridging the gap between how users ask questions ('emails from Sarah last week about the Q3 budget') and what your search API requires to constrain retrieval ({ "author": "Sarah", "date": {"$gte": "..."}, "topic": "Q3 budget"}). The ideal user is a developer or platform engineer integrating an LLM into a hybrid search pipeline where vector similarity alone is insufficient, and precise, database-style filtering is required to scope the retrieval corpus before semantic ranking.

Use this prompt when you have a defined metadata schema with known fields, types, and valid operators, and you need the model to reliably extract constraints from free-text input. It is designed for production systems where the output must be machine-readable and validated before being passed to a search backend like Elasticsearch, Pinecone, or PostgreSQL. You should not use this prompt when the user's query is purely conceptual with no filterable attributes, when your metadata schema is undefined or changes unpredictably, or when the cost of an incorrect filter (e.g., missing a critical document due to an overly strict constraint) is unacceptable without a human-in-the-loop review step. It is also not a replacement for a full query understanding system if you need deep intent classification or multi-hop reasoning; pair it with an intent router if the same endpoint must handle both filtered search and open-ended Q&A.

Before implementing, ensure you have a strict, versioned metadata schema to pass as [SCHEMA]. The prompt's value comes from its ability to reject unknown fields and normalize values, but it can only do that if the schema is explicit. Plan for validation: the output must be checked against your schema before execution, and you should log extraction confidence scores to monitor drift. If a query contains constraints that map to high-risk filters (e.g., compliance holds, legal holds, or financial periods), route the extracted filter for human approval before retrieval. Start by copying the template, inserting your schema, and running it against a golden dataset of 50-100 labeled queries to measure field extraction accuracy and operator correctness before production traffic.

This template is the core instruction set for extracting structured filter clauses from user queries. It expects a natural language input, a known metadata schema, and a set of output constraints. The prompt is designed to be dropped into a larger RAG pipeline where the extracted filters will be passed to a search backend such as Elasticsearch, Pinecone, or PostgreSQL. The square-bracket placeholders must be populated by your application before the prompt is sent to the model.

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You are a metadata constraint parser for a search system. Your job is to extract structured filter clauses from a user's natural language query.

## INPUT
User Query: [USER_QUERY]

## AVAILABLE METADATA SCHEMA
You may only reference fields, operators, and values that exist in the following schema. Do not invent fields or operators.

[SCHEMA_DEFINITION]

## OUTPUT SCHEMA
Return a valid JSON object with this structure:
{
  "filters": [
    {
      "field": "string (must match schema)",
      "operator": "string (one of: eq, neq, gt, gte, lt, lte, in, not_in, between, contains, starts_with, ends_with)",
      "value": "string | number | boolean | array (type must match schema field type)",
      "confidence": 0.0-1.0
    }
  ],
  "unmapped_terms": ["terms from query that could not be mapped to any schema field"],
  "parse_notes": "brief explanation of extraction decisions"
}

## CONSTRAINTS
[CONSTRAINTS]

## EXAMPLES
[EXAMPLES]

## INSTRUCTIONS
1. Identify explicit and implicit constraints in the user query.
2. Map each constraint to the correct field in the schema.
3. Select the appropriate operator based on the constraint type.
4. Normalize values to match the schema field type.
5. Assign a confidence score: 0.9-1.0 for explicit matches, 0.7-0.89 for strong inferences, 0.5-0.69 for weak inferences, below 0.5 for guesses.
6. List any terms you could not map in `unmapped_terms`.
7. If the query contains no filterable constraints, return an empty `filters` array.
8. Do not hallucinate fields, operators, or values that are not in the schema.

To adapt this template, populate [SCHEMA_DEFINITION] with a JSON or structured description of your searchable fields, their types, and allowed values. For [CONSTRAINTS], add rules specific to your domain—such as 'dates must be ISO 8601' or 'price values must be positive numbers.' The [EXAMPLES] block should include 2-4 few-shot demonstrations showing correct extraction for queries typical of your application. After the model returns output, validate the JSON structure, confirm all field names exist in your schema, and check that operator-value-type combinations are valid before passing filters to your search backend. For high-stakes applications, route low-confidence extractions (below your threshold) to human review or discard them and fall back to unfiltered retrieval.

The Metadata Constraint Parsing prompt is not a standalone chatbot interaction. It is a structured extraction step that sits between the user's natural language query and your search backend's filter API. The prompt expects a user query and a known metadata schema as input, and it must return a validated filter object that your application can pass directly to Elasticsearch, OpenSearch, Pinecone, PostgreSQL, or a custom search service. Treat this prompt as a deterministic component in a retrieval pipeline: same input schema plus same query should produce a structurally consistent filter object, even if the model's phrasing varies slightly across runs.

Wire the prompt into your application with a pre-validation guard and a post-extraction validator. Before calling the model, inject the available metadata field definitions, their types (string, date, number, boolean, keyword array), and any allowed enum values into the [SCHEMA_DEFINITION] placeholder. After the model returns JSON, validate that every field name exists in your schema, every operator is supported for that field type, and every value conforms to the expected type. Reject filters that reference unknown fields or use unsupported operators—do not pass them to your search backend. For high-throughput systems, implement a retry loop with a maximum of 2 attempts: if validation fails, feed the validation error message back into the prompt's [PREVIOUS_ERRORS] placeholder and request a corrected output. If the second attempt also fails, fall back to an empty filter and log the failure for review.

Model choice matters here. Use a model with strong JSON mode or structured output support (GPT-4o, Claude 3.5 Sonnet, or Gemini 1.5 Pro with response_schema enabled). Set temperature=0 to minimize variation in filter generation. Enable structured output mode if your provider supports it, passing the exact filter schema as the expected response format. For latency-sensitive applications, consider a smaller model fine-tuned on your specific metadata schema, but only after you have collected at least 500 validated extraction examples from production traffic. Log every extraction—the raw query, the generated filter, validation pass/fail, and the final filter sent to the search backend—so you can measure extraction accuracy, identify schema gaps, and build eval datasets over time.

Tool integration is the preferred production pattern. Instead of parsing free-text JSON from the model response, define the filter output as a tool/function with typed parameters. The model calls apply_metadata_filter(filters: FilterObject) where FilterObject is your typed schema. This eliminates JSON parsing errors and lets the platform enforce type safety before your code runs. If you are using a RAG framework like LangChain or LlamaIndex, wrap this prompt as a query transformation step in the retrieval chain, executing before the vector or keyword search call. Human review is not required for low-risk queries (product search, documentation lookup), but for regulated domains (healthcare, legal, finance), route extractions with confidence below your threshold or filters touching sensitive fields to a review queue before retrieval executes.

What to avoid: Do not pass raw model output directly to your search backend without validation. Do not assume the model will never hallucinate field names—schema injection via the [SCHEMA_DEFINITION] placeholder is your primary defense. Do not skip logging; without extraction traces, you cannot debug why certain queries return empty results. Start with a narrow schema (5-10 fields), validate aggressively, and expand the schema only after reviewing production extraction accuracy over at least a week of live traffic.