What Is RAG?
Retrieval-Augmented Generation (RAG) combines the power of large language models with external knowledge retrieval. Instead of relying solely on training data, RAG systems fetch relevant documents at query time and inject them into the LLM context — producing accurate, up-to-date, and domain-specific answers.
The Vectorization Pipeline
- Document ingestion: Load PDFs, web pages, databases, or APIs as raw text.
- Chunking: Split documents into smaller segments (256–1024 tokens) with overlap for context continuity.
- Embedding: Convert each chunk into a dense vector using models like OpenAI text-embedding-3-small, Cohere embed-v3, or open-source alternatives.
- Storage: Index vectors in a vector database for fast similarity search.
- Retrieval: At query time, embed the user question and find the top-k most similar chunks.
- Generation: Pass retrieved chunks + question to the LLM for a grounded response.
Best Practices
- Use semantic chunking instead of fixed-size splits for better retrieval quality.
- Add metadata filters (date, source, category) to narrow search scope.
- Implement reranking with cross-encoder models to improve top results.
- Monitor retrieval precision and hallucination rates in production.