Introducing AI Query Expansion: How We Made AI Agents 10x Smarter
Our breakthrough AI query expansion technology automatically understands user intent, even when they phrase questions differently. Learn how we built this game-changing feature.

Ever asked an AI chatbot a question and gotten a completely irrelevant answer? We've all been there. The problem isn't the AI's knowledge — it's understanding what you're actually asking.
The Intent Gap
Traditional chatbots match your question against their knowledge base using keyword matching or basic semantic search. But humans don't speak in keywords. We use:
- Synonyms: "How do I cancel?" vs "How do I terminate my subscription?"
- Implicit context: "It's not working" (What's "it"?)
- Conversational language: "Can I get my money back?" vs "What is the refund policy?"
This creates an "intent gap" — the difference between what you mean and what the AI understands.
Our Solution: AI Query Expansion
Query expansion is a technique that automatically transforms your question into multiple related queries, dramatically improving the chances of finding relevant information.
How It Works
When you ask: "How do I cancel?"
Our system expands this to:
- "How do I cancel my subscription?"
- "How to terminate my account?"
- "Cancel membership process"
- "Steps to end my plan"
- "Unsubscribe from service"
Then it searches for all of these, combines the results, and uses AI to synthesize the best answer.
The Technical Magic
We use a multi-stage approach:
1. Intent Classification
First, we classify the type of question:
- Informational ("What is...")
- Procedural ("How do I...")
- Transactional ("I want to...")
- Navigational ("Where is...")
2. Entity Extraction
We identify key entities and their relationships:
- Actions: cancel, refund, upgrade
- Objects: subscription, account, plan
- Modifiers: immediately, partial, full
3. Expansion Generation
Using a fine-tuned LLM, we generate semantically similar queries that cover different phrasings of the same intent.
4. Hybrid Search
We run both semantic (vector) and keyword searches across all expanded queries, then combine results using Reciprocal Rank Fusion.
Real Results
In our testing across 10,000 customer support queries:
| Metric | Before | After | Improvement |
|---|---|---|---|
| Relevant Answer Rate | 67% | 94% | +40% |
| First-Response Resolution | 45% | 78% | +73% |
| Customer Satisfaction | 3.2/5 | 4.6/5 | +44% |
Try It Yourself
Query expansion is now enabled by default for all Chatsy agents. No configuration needed — it just works.
Want to see it in action? Ask our support agent a question in different ways and watch how it consistently finds the right answer.
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