Retrieval-Augmented Generation (RAG)

Why retrieval matters: ground an LLM in your own documents to reduce hallucination and answer questions about private or fresh data.

Embeddings turn text into vectors so that similar meaning lands close together. They are the backbone of retrieval, search, and clustering.

Garbage in, garbage out. How you parse documents and split them into chunks has more impact on RAG quality than almost any other choice.

Wire the pieces together: embed the query, retrieve relevant chunks, stuff them into the prompt, and let the model answer with citations.

Pure vector search misses exact keywords and acronyms. Combine dense and sparse retrieval, query rewriting, and metadata filters for better recall.

A two-stage approach: retrieve a wide set of candidates cheaply, then use a reranker to put the truly relevant chunks at the top before generation.

Chunks lose meaning when torn from their document. Contextual retrieval prepends chunk-specific context before embedding, sharply improving retrieval accuracy.

Fine-tuning changes the model's behavior; RAG changes what it knows. Learn what each is good at and how to choose, or combine, them.