Language models can answer confidently, but they do not automatically know private documents or the latest information. RAG (retrieval-augmented generation) solves this by first finding relevant passages and giving them to the model as context. 🤖
The key component is the embedding: a numerical representation that captures a piece of text’s meaning. Chunks with similar ideas end up close together in vector space. L1, L2, and cosine similarity can measure that closeness.
A practical implementation can store those vectors in PostgreSQL with PGVector. The system splits documents into chunks, calculates embeddings, indexes them, and retrieves the closest matches when a question arrives. AWS Lambda can automate indexing whenever new files appear. ⚙️
💡 Explanation in a nutshell#
Imagine a smart library: instead of asking the model to memorize every book, RAG finds the pages related to your question and shows them before generating an answer. This reduces made-up responses and makes it possible to work with your own knowledge.
Chunk size, context amount, and continuous evaluation directly influence the final quality.
More information at the link 👇
