RAG
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What is RAG?
Retrieval-Augmented Generation (RAG) is an AI approach that combines information retrieval with language generation to create more accurate, contextually relevant outputs. It works by first retrieving relevant data from a knowledge base or external source, then using a language model to generate a response based on that information. This method enhances the accuracy of generative models by grounding their outputs in real-world data, making RAG ideal for tasks like question answering, summarization, and chatbots that require reliable, up-to-date information.
Free resources to learn RAG
More in stage 2: How LLMs Work
RAG: common questions
Can I learn RAG in Hindi?
This lesson is written in English. Once you sign in, open it in the interactive course and ask the AI tutor to explain RAG in Hindi or Hinglish. The tutor answers from this lesson’s own text, so it stays on the syllabus, and it runs on your own Google, OpenAI or Anthropic key.
Is this RAG tutorial for beginners?
It is lesson 17 of 189 in the free AI Engineer roadmap, in stage 2, How LLMs Work. If you are new to AI Engineer, start from stage 1 and come back here in order; if you already know the earlier stages, this lesson stands on its own.