Image created with Ideogram v3. Image prompt: Late‑90s boy‑band cover “RAG‑n‑Roll”: graffiti alley with glowing text snippets on wall; denim vests with patchwork lyrics; portable cassette players; chrome spray‑paint logo.
“🎥🤖 Multi-Modal RAG with Gemma 3 Build a powerful RAG system that processes mixed-content PDFs using Google’s Gemma 3 and LangChain. This implementation combines PDF processing with multi-modal support, powered by Streamlit and Ollama. Check out the tutorial 📚 https://x.com/LangChainAI/status/1916537826050498786
“Really incredible detective work by @singhshiviii et al. at @Cohere_Labs and elsewhere documenting the ways in which @lmarena_ai works with companies to help them game the leaderboard. https://x.com/BlancheMinerva/status/1917445722380681651
(4) Observability for RAG Agents: Evals and monitoring https://decodingml.substack.com/p/observability-for-rag-agents
“RAG systems struggle with high storage costs and slow retrieval times due to large embedding dimensions. This paper uses Principal Component Analysis (PCA) to shrink these embeddings significantly. Compressing embeddings to 110 dimensions speeds up retrieval (up to 60x) and https://x.com/rohanpaul_ai/status/1916386078006591860
“This study shows how NotebookLM, using RAG, acts as a reliable AI physics tutor by grounding answers in teacher-provided documents, promoting collaborative learning. 📌 RAG drastically improves factual grounding, making the LLM reliable for tutoring. 📌 A ‘Training Manual’ https://x.com/rohanpaul_ai/status/1916336501366927409
