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RAG - retrieval-augmented generation

Presentation - Empowering GenAI with RAG

RAG is an AI framework for retrieving facts from an external knowledge base to ground large language models (LLMs) on the most accurate, up-to-date information and to give users insight into LLMs' generative process.

  • RAG combines retrieval and generation processes to enhance the capabilities of LLMs
  • In RAG, the model retrieves relevant information from a knowledge base or external sources
  • This retrieved information is then used in conjunction with the model's internal knowledge to generate coherent and contextually relevant responses
  • RAG enables LLMs to produce higher-quality and more context-aware outputs compared to traditional generation methods
  • Essentially, RAG empowers LLMs to leverage external knowledge for improved performance in various natural language processing tasks

Why is Retrieval-Augmented Generation important​

  • You can think of the LLM as an over-enthusiastic new employee who refuses to stay informed with current events but will always answer every question with absolute confidence.
  • Unfortunately, such an attitude can negatively impact user trust and is not something you want your chatbots to emulate!
  • RAG is one approach to solving some of these challenges. It redirects the LLM to retrieve relevant information from authortative, pre-determined knowledge sources.
  • Organizations have greater control over the generated text output, and users gain insights into how the ML generates the response.

Codes​

Advanced​

RAG from Scratch

Advanced RAG Techniques​

Advanced RAG Techniques: Unlocking the Next Level | by Tarun Singh | Medium

RIG - Retrieval Interleaved Generation - DataGemma through RIG and RAG - by Bugra Akyildiz