RAG explicado: cómo darle a un LLM tu propia información

RAG, or Retrieval-Augmented Generation, is a technique that allows a language model to access and utilize your own information to provide more accurate responses. The model itself doesn't have knowledge of your specific company documents, policies, or products. When asked a question, the model might fabricate a response that sounds convincing, even if it's incorrect.

RAG addresses this issue by incorporating your information directly into the model's response generation process. Here's how it works:

1. Indexing: First, your documents are broken down into smaller fragments and converted into numerical embeddings, which capture the meaning of each piece. This process uses libraries like sentence-transformers and is stored in a vector database such as FAISS or a managed service.

2. Answering questions: When a question is posed, the system searches for the most similar fragments to the query within the vector database. These relevant fragments are then passed to the language model along with the original question. The model generates a response based on the provided context.

3. System prompt: The system prompt instructs the model to respond solely using the provided context. If a fragment containing the answer doesn't exist, the model should indicate that it doesn't have enough information.

The key benefits of RAG include:

- Improved accuracy when answering questions based on your specific information

- Onboarding new users using a chatbot that understands your processes

- Providing detailed, source-cited responses to build trust

- Cost-effective implementation without the need to retrain the entire model

In practice, RAG systems are often built using Python and libraries such as sentence-transformers and FAISS. Xiliux, for example, offers end-to-end RAG solutions tailored to your specific needs. If you have information you'd like to integrate into an AI assistant, Xiliux can help you build a customized RAG system.