Structured query generation

A technique for converting natural language queries into formal, machine-executable query structures (e.g., SQL, boolean logic) to improve retrieval precision and recall in information systems.

Key Applications

• Enhances retrieval-augmented-generation-rag systems by generating optimized queries for database/document retrieval
• Increases recall in search systems by reducing semantic ambiguity in user intent
• Enables complex multi-source data retrieval from heterogeneous repositories

Case Study: RAG Accuracy Improvement

• Client project boosted RAG recall from 50-60% to >95% through:
  • LLM-driven advanced data indexing (preprocessing and semantic tagging)
  • Implementation of Structured query generation for query refinement
• Initial RAG setup: Classic customer service chatbot retrieving from fragmented databases/document repositories without structured queries

Technical Implementation

• Uses large-language-models to transform user queries into:
  • SQL for relational databases
  • Boolean operators for document search
  • Vector similarity constraints for semantic matching
• Requires domain-specific schema knowledge for accurate query translation

Related Concepts

• retrieval-augmented-generation-rag
• Information Retrieval
• Query Optimization
• data-indexing

2026 04 14 Improving RAG accuracy for retrieval

Source Notes

• 2026-04-14: How to get TACK SHARP photos with any camera!