Your team built a knowledge base. SOPs, process docs, meeting notes, historical decisions.
Someone searches "how do we handle refunds" and gets zero results.
The answer exists. It is in a document titled "Customer Service Escalation Procedures, Section 4.2."
The system found nothing because the question used different words than the document.
INTELLIGENCE LAYER - Bridges the gap between how people ask and how documents are written.
Query transformation takes what someone types and rewrites it into forms more likely to find relevant documents. A single question becomes multiple variations. Abbreviations expand. Synonyms appear. Context gets added.
The goal is bridging vocabulary mismatch. Users ask questions in their language. Documents are written in their own vocabulary. Without transformation, perfectly good answers hide in plain sight because the words do not align.
Every knowledge retrieval system eventually hits the wall: the answer exists, but the search cannot find it. Query transformation is how you break through that wall.
Query transformation solves a universal problem: how do you find information when you do not know the exact words it uses? The same pattern appears anywhere human intent must match stored information.
Take the input. Generate multiple variations that preserve meaning but vary vocabulary. Search with all variations. Combine results. The right answer surfaces even when wording differs.
Select a question below. Watch how the system rewrites it multiple ways to find the answer hidden behind different vocabulary.
Add synonyms and related terms
"How do I request time off" expands to include "PTO," "vacation," "leave request," "absence." The expanded query casts a wider net, catching documents that use any of these terms.
Ask the same question multiple ways
An LLM rewrites the original question into 3-5 alternative phrasings. Each version searches independently. Results merge, with documents appearing in multiple result sets ranking higher.
Generate what the answer might look like
Instead of searching with the question, generate a hypothetical answer and search with that. The generated text is closer in style to actual documents, improving embedding similarity.
A support team member asks this question. The answer exists in "Revenue Recognition & Subscription Cancellation Procedures" but a direct search finds nothing. Query transformation rewrites the question, adds synonyms, and generates variations until the right document surfaces.
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You add every synonym from a thesaurus. "Refund" expands to include "reimbursement," "compensation," "payback," "rebate." Now HR compensation documents pollute results for customer refund questions. Users learn to distrust the search.
Instead: Test expansion terms against your actual corpus. Remove terms that pull in unrelated documents.
The LLM rewrites "our Q4 budget process" as "fourth quarter financial planning procedures." But your documents use "annual budget cycle" and "fiscal planning." The rewrite sounds professional but misses how your organization actually talks.
Instead: Fine-tune prompts with examples from your actual document vocabulary. Sample real documents in context.
HyDE works brilliantly for conceptual questions but destroys precision for exact lookups. Someone searches for "Policy 2024-017" and gets a generated paragraph about policies instead of the exact document match.
Instead: Classify query intent first. Use keyword matching for exact lookups, semantic transformation for conceptual questions.
You have learned how to bridge the gap between how people ask questions and how documents are written. The natural next step is combining these transformed queries with other search strategies.