Technical15 min read
RAG in the real world: making your knowledge base useful
Practical tips for implementing retrieval-augmented generation that actually works.
The RAG Challenge
RAG sounds simple: retrieve relevant docs, augment the prompt, generate a response. In practice, it's tricky. Poor retrieval leads to wrong answers. Too much context overwhelms the model. Bad chunking loses critical information.
Getting Retrieval Right
- •Chunk Intelligently: Split documents at natural boundaries, not arbitrary limits
- •Hybrid Search: Combine semantic search with keyword matching
- •Metadata Matters: Tag documents with context—date, author, department
- •Test Relentlessly: Build a test suite of real questions and expected answers
Pro Tip
Start with high-quality, well-structured documents. RAG can't fix bad source material. Clean your knowledge base first, then build the RAG system.
Making It Production-Ready
Monitor retrieval quality continuously. Track which documents are used, which queries fail, and where users report problems. Use this feedback to refine your system over time.