Retrieval-Augmented Generation (RAG) has emerged as a cornerstone technique for building AI systems that need access to up-to-date or proprietary information. By combining a dense vector retrieval step with a generative language model, RAG allows applications to fetch relevant documents from a knowledge store and condition the model’s output on that evidence.
The typical RAG pipeline starts with embedding the user query into a vector space, then performing a similarity search against a pre-indexed corpus. The top-k retrieved passages are concatenated with the original prompt and passed to the LLM. This approach reduces hallucination and provides grounded answers with citations.
Engineering a production RAG system involves choices around chunking strategy, embedding model selection, and re-ranking. For example, smaller chunks improve retrieval precision but may lose context, while larger chunks preserve context at the cost of noise. Evaluating with domain-specific benchmarks is critical to balance these trade-offs.