Improving RAG: Strategies
Michael Iversen added
Retrieval-augmented generation (RAG) is a technique that enhances text generation by retrieving and incorporating external knowledge.
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
- Query the RAG anyway and let the LLM itself chose whether to use the the RAG context or its built in knowledge
- Query the RAG but only provide the result to the LLM if it meets some level of relevancy (ie embedding distance) to the question
- Run the LLM both on it's own and with the RAG response, use a heuristic (or another LLM) to pick the best answer
r/LocalLLaMA - Reddit
Nicolay Gerold added
Retrieval-Augmented Generation (RAG): A Technical AI Explainer
youtube.comRagas is a framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines. RAG denotes a class of LLM applications that use external data to augment the LLM’s context. There are existing tools and frameworks that help you build these pipelines but evaluating it and quantifying your pipeline performance can be hard. This is wh... See more
explodinggradients • GitHub - explodinggradients/ragas: Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines
Nicolay Gerold added
Welcome - GraphRAG
microsoft.github.iokaiton added
Sounds fancy. Why do we care? GAR involves taking the source documents and having an LLM enrich them, prior to indexing. For example, the LLM might... * Generate titles for documents that are missing them * Standardize author names/formats* Extract dates, URLs, citations and other elements that might be valuable to search as separate fields* Create... See more