Query Processing – Making RAG Actually Understand You
If youve been following along, you now know why LLMs hallucinate — theyre probabilistic prediction engines, not databases. They guess what comes next based on statistical patterns learned from internet.
The solution? Stop making them guess. Give them access to your data.
In Part 2, we explored two approaches: RAG and CAG. RAG (Retrieval-Augmented Generation) retrieves relevant documents at runtime, while CAG (Cache-Augmented Generation) pre-loads knowledge into models context window.
If youre building something serious — especially with large, dynamic knowledge bases — RAG is the right choice. And at the heart of every RAG system lies a vector database.
The Problem: Users Ask Terrible Questions
When someone types how do i deploy k8s into a vector database, theyre essentially speaking Greek. The retrieval system returns… nothing relevant.
Real Examples of Query Failures
User query: How do I troubleshoot my database?
System retrieval: Returns general database troubleshooting docs
User thinks: Wait, my question about specific tech stack
The Solution: Query Processing
1. Query Decomposition: Breaking It Down
Complex queries need to be broken into manageable pieces. Identify intent and extract key entities.
Step A: Identify Intent
What does the user want?
- Factual information
- Procedural help
- Creative/brainstorming
- Troubleshooting/debugging
Step B: Determine Scope
Is this about code, data, system architecture?
- Code-specific
- Data-related
- System-wide
Step C: Resolve Ambiguity
What do they mean by database? SQL? NoSQL? Vector?
Query: Compare vector databases
Context: User is evaluating options (FAISS, Pinecone, Weaviate, Qdrant)
Resolution: Explicitly search for comparison tables, not individual vector DB internals
2. Query Routing: Intent Classification
Categorize queries: factual, procedural, creative, troubleshooting
Route to different retrieval strategies
3. Query Expansion: Making It Smarter
Users rarely search with perfect terminology. Query expansion adds synonyms and related terms.
4. Re-ranking: Quality Control
Initial retrieval may return noisy/irrelevant results. Re-ranking optimizes for relevance and diversity.
5. Context Management
A good query processor maintains conversation history and uses previous interactions to improve future retrievals.
6. Prompt Engineering for RAG
The right prompt architecture can make or break your RAG system.
Common RAG Prompt Mistakes
- No system instructions: Heres a document about X, answer based on it
- Ignoring retrieval scores: Not using distance/similarity metadata
- Letting it hallucinate: Feel free to use your general knowledge if context doesnt have answer
- Not saying I dont know: Better to say Based on the provided context, I dont have that information and offer alternative perspectives
Query Processing in RAG Pipeline
User asks a question → Goes to vector DB
Decompose → Expand → Route → Retrieve → Re-rank → Generate
Key Insight: Query Processing is the unsung hero of RAG systems.
Most people focus on embeddings and vector databases. But smart RAG systems win or lose on query processing. A great retrieval with wrong routing is useless.
Connecting the Dots: Bridge to Part 5
In Part 4, we explored how to make RAG actually understand user queries through intelligent query processing. Part 5 will show you how to build a RAG system that doesnt just store vectors, but thinks.
This is Part 4 of a 7-part series on AI & RAG.