Noise Word and Filler Removal Prompt Template

This prompt is for search engineers and RAG operators who need to strip conversational filler, politeness phrases, and non-substantive tokens from user queries before they hit an embedding model or keyword index. The job-to-be-done is pre-retrieval query cleaning: turning 'Um, can you please tell me what the revenue was, like, last quarter?' into 'revenue last quarter' without losing critical modifiers, negations, or domain-specific terms. The ideal user is someone managing a production retrieval pipeline where noisy user input—from voice-to-text errors, chat interfaces, or support tickets—degrades recall and wastes compute on irrelevant vector searches.

Do not use this prompt when the filler words carry semantic weight. For example, in sentiment analysis or tone detection, words like 'unfortunately' or 'honestly' are signals, not noise. Do not use it for queries where politeness level indicates urgency or user state that should be preserved for downstream routing. This prompt is also inappropriate for legal or compliance workflows where the exact user phrasing must be retained for audit trails. In those cases, log the original query and run the cleaned version as a parallel retrieval branch rather than replacing the source of truth.

Before wiring this into production, pair it with a validation step that checks for accidental removal of query-critical modifiers—negations like 'not' or 'never,' numerical qualifiers like 'more than' or 'under,' and domain abbreviations that look like filler. Run a semantic drift eval comparing the original and cleaned query embeddings against a threshold. If the distance exceeds your safety margin, route to a human review queue or fall back to the original query. Start with a conservative removal policy and tighten only after reviewing production traces.

The prompt template below is designed to be dropped directly into your retrieval pipeline. It accepts a raw user query and returns a cleaned version that preserves the core search intent while removing politeness phrases, conversational filler, and non-substantive tokens that degrade retrieval quality. The template uses square-bracket placeholders so you can wire in your own input, constraints, and output schema without modifying the core instruction logic.

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You are a query cleaner for a search system. Your job is to remove noise words, conversational filler, and politeness phrases from user queries while preserving the core search intent.

INPUT QUERY:
[USER_QUERY]

INSTRUCTIONS:
1. Remove greetings, sign-offs, and politeness phrases (e.g., "hello", "please", "thank you", "can you help me with", "I was wondering if").
2. Remove filler words that do not change the search meaning (e.g., "um", "like", "just", "actually", "basically").
3. Remove conversational framing that does not contain search-relevant content (e.g., "I have a question about", "I need to find").
4. Preserve all domain-specific terms, technical jargon, proper nouns, and query-critical modifiers (e.g., "not", "only", "exact", "latest", "best").
5. Preserve the original word order of the remaining content words.
6. Do not add, infer, or expand any terms. Only remove noise.

CONSTRAINTS:
[CONSTRAINTS]

OUTPUT FORMAT:
Return a JSON object with the following fields:
{
  "cleaned_query": "<the stripped query string>",
  "removed_tokens": ["<list of removed words or phrases>"],
  "preserved_modifiers": ["<list of query-critical modifiers that were kept>"],
  "confidence": <float between 0.0 and 1.0>
}

OUTPUT ONLY THE JSON OBJECT. NO OTHER TEXT.

To adapt this template, replace [USER_QUERY] with your raw input string. Use [CONSTRAINTS] to inject domain-specific rules, such as a list of protected terms that must never be removed or a maximum allowed edit distance. For high-stakes retrieval pipelines where accidental removal of a query-critical modifier could cause missed results, add a human-review step or a semantic drift check comparing the cleaned query embedding to the original query embedding before executing retrieval. Test this prompt against a golden dataset of noisy queries with known clean equivalents to calibrate your confidence threshold.

Integrating the noise word removal prompt into a production RAG system requires treating it as a pre-retrieval transformation step with clear input/output contracts. The prompt should be called immediately after user input is received and before any embedding, keyword extraction, or metadata filter generation occurs. The application layer must supply the raw user query as the [RAW_QUERY] input, an optional [DOMAIN_TERMS] list to protect query-critical modifiers from accidental removal, and a [STRIP_LEVEL] parameter (e.g., conversational, aggressive, or conservative) that controls how aggressively filler is removed. The model's response must conform to a strict JSON schema containing the cleaned_query string, a removed_tokens array for auditability, and a confidence_score between 0 and 1. This structured output allows downstream components to decide whether to use the cleaned query, fall back to the original, or request human review based on the confidence threshold.

Validation should occur in two stages. First, validate the JSON structure and field types immediately after the model responds. If the output fails to parse, retry once with the same prompt and a stronger format instruction appended to the system message. Second, apply semantic validation: compare the embedding of the cleaned_query against the original [RAW_QUERY] using cosine similarity. If the similarity drops below a configurable threshold (start with 0.85 and tune on your domain data), flag the transformation for review and log the incident. This catches cases where the model removed a domain-critical modifier like 'not' or 'only' that it incorrectly classified as filler. For high-stakes domains such as clinical or legal search, route any query where confidence_score < 0.9 or where removed_tokens contains terms from the [DOMAIN_TERMS] safelist to a human-in-the-loop review queue before retrieval executes. Log every transformation with the original query, cleaned query, removed tokens, confidence score, and similarity score for offline analysis and prompt iteration.

Model choice matters for this task. Smaller, faster models (e.g., GPT-4o-mini, Claude Haiku, or fine-tuned open-weight models) are usually sufficient for filler removal since the task is classification-like rather than generative. This keeps latency low and cost predictable in the hot path before retrieval. If your system already uses a larger model for answer generation, avoid routing the cleaning step through it to prevent unnecessary token consumption. For applications with strict privacy requirements, run this prompt on a locally deployed model and ensure the removed_tokens log does not inadvertently retain PII from the original query. Set a hard timeout of 500-800ms for this step; if the model does not respond in time, fall back to the original query and increment a cleaner_timeout metric. Never block the user's retrieval request waiting for a query cleaning step that has exceeded its latency budget.