Results for “retrieval augmented generation”

…RAG (yes I already watched the IBM video) (Activity: 28): RAG (Retrieval-Augmented Generation) is a technique that enhances language models by integrating a retrieval mechanism to efficiently handle large datasets. It involves using an…

…is available on GitHub for users looking to create their own retrieval augmented generation systems. One user shares their approach to building a RAG system, using LangChain doc loaders, Qdrant, OpenAI embeddings, and Llama 3 8B. Nous…

…Gets Thumbs Up, But Has Caveats: The channel debated using RAG (Retrieval-Augmented Generation) to summarize issues, with one member suggesting summarizing closed issues into RAG for long-term memory. Concerns were raised that embeddings…

…choosing serverless providers and developments in the market. Retrieval Augmented Generation (RAG) Potential: Discussion highlighted that fine-tuning Llama 3.1 could be effective when executing any model on Axolotl, with RAG viewed as a…

…for money to come in GPU MODE ▷ #🍿 (2 messages): Dataset Generation, Diverse Datasets, Augmented Datasets New Dataset Generators emerge!: A member shared a new dataset generator project, noting they lost count of how many times they've had…

…potentially impacting startups and academia (Command-R: Retrieval Augmented Generation at Production Scale). AI-Centric Dune Casting Game Unfolds: Discord members humorously cast prominent figures from the AI industry as characters from…

…amount of lore, and community members suggested RAG (Retrieval-Augmented Generation) with text similarity search via LocalDocs as a potential solution. This approach would allow the user to retrieve relevant lore segments based on…

…and the “Agent Swarm” orchestration concept (dynamic generation of subagents; up to 100 parallel subagents / 1,500 steps; wall-time improvements claimed 3–4.5×) is summarized by @TheZachMueller (and points to the technical report).…

…the tug-of-war between RAG and LLMs' internal prior: Retrieval augmented generation (RAG) is often used to fix hallucinations and provide up-to-date knowledge for large language models (LLMs). However, in cases when the LLM alone…

…system for investment research. @jerryjliu0 discusses universal retrieval APIs for LlamaCloud agents to access enterprise context. @omarsar0 reviews a memory-augmented LLM OS for better agent memory management. Agentic search and economic…

…not just a model problem. The model provides reasoning and generation, but production quality comes from: dynamic tool selection, memory architecture, evaluation/review loops, error taxonomy and retries, model-specific prompt branching,…

…and perform precise video editing. AI Research and Applications Retrieval Augmented Generation (RAG): @LangChainAI shared an implementation of a Retrieval Agent using LangGraph and Exa for more complex question/answering applications. AI in…