Retrieval-Augmented Generation (RAG) is gaining prominence as a technique to provide context to Large Language Models (LLMs). It involves dynamic retrieval, critique, and generation, enabling LLMs to connect with external data sources. Various techniques such as Self-RAG, Corrective RAG, and Self-Reflective RAG are being explored to enhance RAG. The pace of innovation in RAG makes it challenging to keep up, leading to the launch of educational video series and workshops. RAG can be implemented by baking data into the model's weights or passing data as input context. The future of Generative AI is seen as decentralized, with RAG being a fundamental technique for LLM applications.
Retrieval Augmented Generation (RAG) is a powerful way to extend LLMs, but to implement it effectively, you need the right retrieval techniques and evaluation metrics. In this workshop, you’ll learn to build better RAG-powered applications faster. RSVP: https://t.co/ulZj6xHe7Z
Corrective RAG Corrective RAG (CRAG) is a recent paper that uses self-reflection to identify and correct problems in retrieval. It first uses a retrieval evaluator to assess the quality of retrieved documents relative to the query. It filters out irrelevant documents and… https://t.co/iuGU1LCp0V
The future of Generative AI is Retrieval-Augmented Generation (RAG). The future of RAG is decentralized ➡️ @origin_trail https://t.co/6cieelzEMy
RAG is fairly fundamental as a technique. If you want to use your data in an LLM application, there are two primary ways to get the data into the LLM. 1. Bake the data into the weights (via training / fine-tuning). 2. Pass the data as input context into the model. These are not… https://t.co/zhvIca0Cba
Self-Reflective RAG using LangGraph Self-reflection can enhance RAG, enabling correction of poor quality retrieval or generations. Several recent papers focus on this theme, but implementing the ideas can be tricky. Here is a video and two cookbooks that show how to engineer… https://t.co/c7M9fcMsfQ
⁉️ RAG with Q&A Retrieval augmented generation has proven itself as one of the best ways to provide relevant & up to date context to LLMs. We just added a new in-depth use case section outlining different RAG techniques in @LangChainAI 🦜🔗! Looking to perform RAG with agents,… https://t.co/VowD3rwmNc
Retrieval Augmented Generation (RAG) is emerging as the preferred technique for bringing context to LLMs We're launching to a new YouTube series to highlight RAG. Will start with the basics, but quickly move onto the advanced concepts (seems like theres a new one each week!) https://t.co/JCtHwesL7j
RAG From Scratch: Video series focused on understanding the RAG landscape RAG is central for LLM application development, connecting LLMs to external data sources. But, the pace of innovation and new approaches makes it challenging to keep up. We're launching a new video… https://t.co/963lOnVLcP
Self-RAG in @llama_index We’re excited to feature Self-RAG, a special RAG technique where an LLM can do self-reflection for dynamic retrieval, critique, and generation (@AkariAsai et al.). It’s implemented in @llama_index as a custom query engine with… https://t.co/bGxXohNagr https://t.co/3YzVJW0Fr8
RAG for LLMs Been doing some deeper exploration into RAG and the ecosystem. I believe a strong starting point is the survey of Gao et al.: "Retrieval-Augmented Generation for Large Language Models: A Survey". I liked the paper so much that I wrote a shorter summary of it to… https://t.co/bckraovatH