Image created with Flux Pro v1.1 Ultra. Image prompt: Assembly instruction diagram for a filing cabinet with automated retrieval system, 1970s office furniture style, olive green and manila folder colors, office manual background, “RAG” in label maker font, drawer slide mechanisms shown, index card system detailed
How we built an AI agent that creates daily content from our existing material (n8n + Cloudflare + AutoRAG setup) We’ve been experimenting with autonomous content workflows inside our business—and one of the most useful agents we built is one that turns raw content into polished https://x.com/jelanifuel/status/1918783103109341352
Very excited to bring AI querying capabilities to all the @vercel observability data. A huge obstacle when jumping in and debugging a system is not the code… it’s learning the o11y query language du jour. You can just ask for things. https://x.com/rauchg/status/1928160175854121390
Obviously naive RAG sucks, but it’s pretty clear that agents still need the right retrieval tools in order to access massive amount of enterprise context. 🤖🔎 This is a fantastic blog post by @thesourabhd showing how we’ve carefully crafted a universal retrieval interface https://x.com/i/web/status/1928183330035425693
The “”R”” in RAG is “”Retrieval,”” which means something, but many seem to conveniently ignore that. If your system (agentic or not) has a retrieval component of any kind (embedding-based or not), it can be considered a RAG system. Optimizing the retrieval component is the hard”” / X https://x.com/omarsar0/status/1927138441122213906
It’s interesting how the major LLM API vendors are converging on the following features: – Code execution: Python in a sandbox – Web search – like Anthropic, Mistral seem to use Brave – Document library aka hosted RAG – Image generation (FLUX for Mistral) – Model Context Protocol”” / X https://x.com/simonw/status/1927378768873550310
LLMs struggle with complex information retrieval in long documents, and existing evaluations lack fine-grained question difficulty. KG-QAGEN framework uses knowledge graphs from financial documents to create a large dataset with systematically varied question complexity, showing https://x.com/rohanpaul_ai/status/1926976611615953022
New Lens on RAG Systems RAG systems are more brittle than you think, even when provided sufficient context. Great work from Google and collaborators. Good tips for devs included. Here are my notes: https://x.com/omarsar0/status/1927737131478188295
RAG is dead, long live agentic retrieval! At LlamaIndex we’ve been saying for a long time that naive RAG is not enough for a modern application. Following from that conviction, we’ve built agentic strategies directly into LlamaCloud that you can adopt with just a few lines of https://x.com/llama_index/status/1928142249935917385
Agent Connectors You can connect tools like web search and code execution to the agents. Other built-in tools include image generation and a document library (accessing documents from Mistral Cloud) for building agentic RAG systems. https://x.com/omarsar0/status/1927369763023396900
