Retrieval-Augmented Generation (RAG) is gaining traction among leading technologists for its ability to enhance large language models (LLMs) by connecting them with real-world data. This approach addresses challenges like hallucinations and rising costs. Various implementations and tools are being introduced to leverage RAG, including DuetRAG, which integrates domain fine-tuning and a referee model to improve knowledge retrieval and generation quality for complex domain-specific tasks. Additionally, RAGApp offers a no-code interface to configure RAG chatbots, making it accessible and deployable in any cloud infrastructure as a Docker container. It is also fully open-source. Weaviate emphasizes the importance of good search to maximize the benefits of RAG. Moreover, an AI agent is available to build RAG systems at scale, simplifying the process for users and supporting datasets from sources like SharePoint.
An AI Agent To Build RAG Systems At Scale! Several folks are still learning how to use RAG. A custom-built AI agent can do it for you at scale!! This video shows how you can - Attach any dataset through any app connector (e.g. SharePoint) - Ask the AI Agent to build a… https://t.co/v0C1znN61g
Introducing RAGApp 💫 A no-code interface to configure a RAG chatbot, as dead-simple as GPTs by @OpenAI. It’s a docker container that’s easily deployable in any cloud infrastructure. Best of all, it’s fully open-source 🔥 1️⃣ Setup the LLM: Configure the model provider (OpenAI,… https://t.co/34ERj5W7Q9
Use RAG: Retrieval Augmented Generation to help large language models produce more specific and better results? Weaviate is built from the ground up for good search, so you can make the most out of RAG. Get @bobvanluijt's thoughts on RAG and what comes next:… https://t.co/6J9aGy0K1J
Why are leading technologists choosing Retrieval-Augmented Generation (RAG) systems for cutting-edge LLM solutions? RAG connects LLMs with real-world data, tackling challenges like hallucinations and rising costs. Explore the top 5 reasons enterprises are choosing RAG systems… https://t.co/ejvCMw7maz
DuetRAG: Collaborative Retrieval-Augmented Generation Integrates domain fine-tuning and RAG models, along with a referee model, to improve knowledge retrieval and generation quality for LLMs in complex domain-specific question-answering tasks. 📝https://t.co/3lxBc3Mb4s https://t.co/EOTofE2fCA
Imagine your language helper getting way smarter by finding the info it needs to answer your questions better. That's the power of RAG! Learn more about RAG and how it can supercharge your language model ▶️ https://t.co/b6SegQ2zxA #RAG #LLM #AI https://t.co/z6ctpItwMr