Learn how to design robust Retrieval-Augmented Generation (RAG) pipelines: chunking strategies, vector embeddings, hybrid semantic search, and reranking models.
These best practices will help you build solutions that are clean, performant, and intuitive for teammates to understand and scale.
Overcoming Naive Vector Search Limitations
Most naive RAG implementations fail in production because simple cosine similarity on dense vectors struggles with exact keyword matches (e.g. part numbers, names, specific codes) and document structure hierarchy.
Building an enterprise-ready pipeline requires parent-document retrieval, recursive semantic chunking, and dual-stage reranking.
Do:Implement semantic chunking with metadata
Preserve document hierarchy, headers, timestamps, and source URLs in vector payload metadata to allow deterministic filtering during retrieval.
Avoid:Fixed-character slicing across sentences
Avoid arbitrary string splits (e.g. 500 characters) that cut sentences in half. Use token-aware recursive sentence splitters.
Starter Templates & Resources
Full-Stack RAG Boilerplate (Node.js & Python)
Includes vector ingestion pipelines, hybrid search ranking, and streaming LLM responses with citations.