Building a RAG Pipeline: A Practical Guide
What is RAG?
Retrieval-Augmented Generation combines a retrieval system with a generative AI model. Instead of relying solely on the model's training data, RAG fetches relevant documents from your knowledge base and uses them as context for generating answers.
Chunking Strategies
The first step is splitting documents into manageable chunks. Recursive character splitting (1000 characters with 200 overlap) works well for most documents. For websites, semantic chunking by headings produces better results.
Embedding and Vector Search
Each chunk is converted into a vector embedding using models like text-embedding-3-small. These embeddings are stored in a vector database (like Qdrant or MongoDB Atlas Vector Search). When a question comes in, it's embedded and used to find the most similar chunks via cosine similarity.
Prompt Construction and Generation
The retrieved chunks are injected into a carefully crafted prompt alongside the conversation history and the user's question. The LLM then generates a response grounded in the provided context, including citations to the source documents.