RAG (Reterival Augemented Generation)

Retrieval-Augmented Generation (RAG) is an AI framework that enhances large language models (LLMs) by integrating an information retrieval component into their response-generation process. Instead of relying solely on the information within the model's original training data, RAG systems dynamically search for and retrieve relevant, up-to-date information from external data sources—such as databases, document repositories, or APIs—when a user query is received. The retrieved data is combined with the user's input to create an enriched prompt, which is then passed to the LLM. This approach yields responses that are more accurate, current, and grounded in factual knowledge.

📄️ RAG

RAG is your intelligent AI research assistant that combines powerful language models with real-time document retrieval to give context-aware, grounded answers. Imagine chatting with an expert who not only understands your question but also instantly pulls precise information from your documents—that’s RAG. It’s privacy-first, lightning-fast, beautifully formatted, and model-flexible—ideal for exploring and understanding your content interactively.

📄️ User guide

Retrieval-Augmented Generation (RAG) allows you to ask questions against your own documents using advanced language models. This guide walks you through each step of the workflow, from deploying the model to retrieving precise, document-based answers.