Exclusion Filter Construction Prompt Template

This prompt is designed for search engineers and RAG system builders who need to translate negative user constraints—phrases like 'without', 'except', or 'not including'—into structured exclusion filters. Vector similarity search alone cannot satisfy negative constraints because it retrieves documents that are semantically close to a query, not documents that explicitly lack a property. When a user asks for 'articles about Python, but not about Django,' the vector embedding for that query will still pull in Django-related content. This prompt bridges that gap by producing machine-readable NOT clauses that can be applied to metadata fields, facets, or keyword indexes, ensuring your retrieval pipeline respects explicit user exclusions alongside positive query intent.

Use this prompt when your retrieval pipeline must enforce hard exclusion rules derived from natural language. Concrete scenarios include: a legal research tool where a user requests 'cases involving patent infringement, not copyright'; an e-commerce search for 'laptops with 16GB RAM, excluding Dell and HP'; or an internal knowledge base query for 'incident reports from Q3, not including severity-4 tickets.' In each case, the negative constraint is a strict filter, not a preference, and failing to apply it produces incorrect results. The prompt expects a user query as input and outputs a structured filter object with include and exclude clauses, field names, operators, and values. You should wire this into your retrieval pipeline before the vector or full-text search step, applying the exclusion filters to your metadata index or post-retrieval result set.

Do not use this prompt for general sentiment analysis or for queries where negative language indicates preference rather than strict exclusion. A query like 'I want something not too expensive' expresses a soft preference, not a hard filter, and applying an exclusion filter here would incorrectly drop mid-range results. Similarly, avoid this prompt when your search index lacks the metadata fields needed to enforce the extracted exclusions—if a user says 'not by unknown authors' but your index has no author field, the filter is inoperable. Start by testing the prompt against a golden set of queries with known negative constraints, and always validate that the output filter clauses reference fields that actually exist in your schema before executing the search.

This template is designed to be copied directly into your prompt library or AI harness. It instructs the model to parse a user's natural language query, identify exclusion patterns such as 'without', 'except', or 'not including', and return a structured list of NOT filter clauses. The output is intended for consumption by a search API or retrieval engine, not for direct display to the user.

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You are an exclusion filter extraction engine. Your task is to analyze the user's query and extract any negative constraints that explicitly exclude certain categories, entities, or attributes.

[INPUT]

[CONTEXT]

[OUTPUT_SCHEMA]

[CONSTRAINTS]

[EXAMPLES]

To adapt this template, replace each square-bracket placeholder with your specific configuration. [INPUT] should contain the raw user query. [CONTEXT] can include the available filterable fields, their types, and valid values from your schema. [OUTPUT_SCHEMA] must define the exact JSON structure you expect, such as an array of objects with field, operator (always 'NOT'), and value keys. [CONSTRAINTS] is where you enforce rules like 'ignore negations that are not explicit exclusions' or 'resolve double negatives into positive filters.' Finally, [EXAMPLES] should provide few-shot demonstrations of correct input-output pairs, including tricky cases like scope ambiguity and double negation. After copying, test the prompt against a golden dataset of queries with known exclusion patterns before integrating it into your retrieval pipeline.

The exclusion filter prompt operates as a pre-retrieval transformation step in a search pipeline. It receives a raw user query and returns structured NOT filter clauses that must be applied to the retrieval backend before executing the search. This prompt is not a standalone chatbot interaction—it is a deterministic extraction component that runs synchronously before query execution. The harness must validate the output schema, handle empty results gracefully, and prevent malformed filters from reaching the search index.

Wire the prompt into your application by placing it after query intake but before retrieval dispatch. The caller must supply the raw user query as [INPUT] and a schema definition as [OUTPUT_SCHEMA] specifying the expected filter structure—typically a JSON array of exclusion objects with field, operator, and value keys. After the model responds, run a structural validator that checks: (1) the response is valid JSON, (2) every exclusion object contains required fields, (3) operators are limited to not_equals, not_in, or not_contains, and (4) no exclusion clause contradicts known inclusion filters from parallel extraction steps. If validation fails, retry once with the error message appended to the prompt context. If the retry also fails, log the failure and fall back to retrieval without exclusion filters rather than blocking the user.

Double-negation handling requires specific harness logic. When the model extracts a clause like 'not without X', the validator must flag it for review rather than passing it directly to the search backend. Implement a post-extraction normalization step that rewrites double-negatives into positive constraints where possible, and quarantines unresolvable cases for human review. For scope boundary detection, the harness should compare exclusion clauses against the active search scope—if a user says 'documents not from 2023' while searching a 2024-only index, the exclusion is redundant and should be dropped to avoid unnecessary filter complexity. Log all dropped and modified clauses for observability.

Model choice matters for this prompt. Use a model with strong instruction-following and JSON output capabilities—GPT-4o, Claude 3.5 Sonnet, or equivalent. Avoid smaller models that struggle with negation logic and scope reasoning. Set temperature=0 for deterministic extraction. If using a model that supports structured output modes, enforce the exclusion filter schema directly through the API rather than relying solely on prompt instructions. For high-throughput systems, cache frequent query patterns and their extracted exclusion filters to reduce latency and cost.

Before shipping, build an eval suite with at least 50 test cases covering: single exclusions ('without PDFs'), multiple exclusions ('not from marketing or sales'), double-negation ('not without approval'), scope edge cases ('except last week' when no date range exists), and adversarial inputs designed to produce contradictory filters. Measure precision (extracted exclusions that are correct), recall (missed exclusions), and the false-positive rate (incorrect exclusions that would block valid results). Set a minimum precision threshold of 95% before deployment. Monitor these metrics in production through sampled trace review, and escalate any regression that drops precision below 90%.