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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

Contextual Retrieval​

Contextual Retrieval (introduced by Anthropic1) addresses a common issue in traditional Retrieval-Augmented Generation (RAG) systems: individual text chunks often lack enough context for accurate retrieval and understanding.

Contextual Retrieval enhances each chunk by adding specific, explanatory context before embedding or indexing it. This preserves the relationship between the chunk and its broader document, significantly improving the system's ability to retrieve and use the most relevant information.

GraphRAG​

Graph RAG