Retrieval-Augmented Generation (RAG) is a powerful pattern that enhances Large Language Models (LLMs) by grounding their responses in your specific documents and data. While GPT-4 is incredibly capable, it doesn’t know about your proprietary documents, internal knowledge bases, or recent updates that occurred after its training cutoff date. RAG solves this problem by retrieving relevant context from your documents before generating responses.
Category Archives: Retrieval Augmented Generation (RAG)
Unlocking the Power of LLMs with LangChain
As an AI and software professional, you’ve likely heard the buzz around large language models (LLMs) like GPT-3, ChatGPT, and their growing capabilities. These powerful models can handle a wide range of natural language tasks, from text generation to question answering. However, effectively leveraging LLMs in your own applications can be a complex challenge. That’s where LangChain comes in.