RAG: ask your documents with AI

RAG (Retrieval-Augmented Generation) is a technique that combines a language model (such as Claude, OpenAI or Ollama) with a database of your own documents, so that the model can answer using real, up-to-date information instead of relying only on what it "knows" from its training.

It works like this:

1. Your documents (PDFs, manuals, product sheets...) are split into fragments and converted into embeddings (numeric vectors that represent the meaning of the text).
2. When someone asks a question, that question is also converted into an embedding, and the database is searched for the fragments that are most semantically similar (not by exact words, but by meaning).
3. The most relevant fragments are passed to the AI model as context, together with the question.
4. The model answers based on that real context, instead of making things up or relying only on its memory.

The advantage is that the model can "consult" specific and private information (your own documents) without having to be retrained.