Retrieval-Augmented Generation (RAG) is gaining prominence as a technique for providing context to Large Language Models (LLMs). It involves dynamic retrieval, critique, and generation, and is being explored in various forms such as Self-RAG and RAG from Scratch. The rapid innovation in RAG techniques presents a challenge for staying updated. RAG can be used to incorporate data into LLMs through techniques like baking the data into the model's weights or passing the data as input context. The future of Generative AI is seen as being closely tied to RAG, with bullish sentiments from industry experts regarding the potential of AI-focused startups in this space.
💬 "Generative AI is a game-changer and those that don’t embrace it will begin to fall behind their competitors." Our CMO, @galancantu, has some really interesting insights about AI for you!👇 https://t.co/Nz7nP5twTI
The future of Generative AI is Retrieval-Augmented Generation (RAG). The future of RAG is decentralized ➡️ @origin_trail https://t.co/6cieelzEMy
#YoungTurks | @LightspeedIndia's @rahultaneja is bullish on the #GenerativeAI wave! He tells @ShereenBhan, "We're limited by the imagination on what AI could do. Capital is not limited for AI-focused startups" #LIFTOFFbyLightspeed @CNBCYoungTurks https://t.co/6v2AQlf2yp
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
⁉️ 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