RAG

What is Retrieval-Augmented Generation?

Retrieval-Augmented Generation, or RAG, is a type of language generation model that combines pre-trained parametric and non-parametric memory for language generation.

For example, the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. RAG models can use the retrieved passages from Wikipedia to generate more specific, diverse and factual language than a state-of-the-art parametric-only seq2seq baseline.

RAG models have been shown to achieve state-of-the-art results on several knowledge-intensive NLP tasks, such as open domain question answering, natural language generation and summarization. RAG models are one of the techniques for grounding language models, which means providing them with relevant information from external sources to improve their performance and explainability.

Azure Cognitive Search: Outperforming vector search with hybrid retrieval and ranking capabilitiesHow do you find the best content to feed your generative AI models? In this blog post, we show you how to use Azure Cognitive Search's hybrid retrieval..

Should I use Prompting, RAG or Fine-tuning? - VellumComparing when Prompting, Retrieval Augmented Generation and Fine-Tuning

What is retrieval-augmented generation? | IBM Research BlogRAG is an AI framework for retrieving facts to ground LLMs on the most accurate information and to give users insight into AI’s decisionmaking process.

Information Retrieval For Retrieval Augmented GenerationThree Battle-Tested Tips to Dramatically Boost Performance

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Understanding Retrieval-Augmented Generation (RAG) empowering LLMsUnderstand innovative artificial intelligence framework that empower large language models (LLMs) by anchoring them to external knowledge sources with accurate, current information.

From RAGs to riches with knowledge graphs?Foundational LLMs like ChatGPT and Llama are general-purpose models that by definition don't necessarily do a good job in specific domains or for very specific tasks. But they're far too expensive and too difficult to retrain for different use cases or different data sets.

Harnessing Retrieval Augmented Generation With LangchainImplementing RAG using Langchain

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Knowledge Graphs & LLMs: Fine-Tuning vs. Retrieval-Augmented GenerationDiscover the limitations of Large Language Models (LLMs), and how to overcome them through fine-tuning vs. retrieval-augmented generation.

LangChain Library Adds Full Support for Neo4j Vector IndexStreamlining data ingestion and querying in Retrieval-Augmented Generation Applications

Introduction To Retrieval Augmented GenerationThis post is written in collaboration with Aparna Dhinakaran and Erick Siavichay Common LLM Use Cases: What’s Real and Not Just a Twitter Demo? Beyond social media demonstrations, LLMs are...

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