A large bucket full of books with the label RAG on the side
“Excited to share our latest research on Contextual Retrieval – a technique that reduces incorrect chunk retrieval rates by up to 67%. When combined with prompt caching, it may be one of the best techniques there is for implementing retrieval in RAG apps. Let me explain:
“Reasoning will be the future of AI, but RAG is the present! How can we improve the faithfulness of LLMs? @SFResearch released ContextualBench, a leaderboard and evaluation framework combining multiple academic RAG benchmarks such as HotpotQA, and announced SFR-RAG 9B, a
“Anthropic released a simple yet elegant technique to improve RAG results: By using an LLM call to attach context to each chunk of content processed, the embedding becomes much more representative of the document in its context. They observed a 67% improvement in the
“You can now point to a bucket of PDFs, Powerpoints and other files in Sharepoint and do RAG over them in minutes – while having full confidence that the system absorbs complex spatial layouts, nested tables, and visual elements like charts and diagrams. The trick: Parse, index,
“Wikimedia Enterprise just dropped full English & French Wikipedia on Hugging Face as structured JSON 🤯 Key points: 1. Parsed articles ready for machine learning pipelines 2. Perfect for AI model development – from pre-training to RAG 3. Includes metadata, Wikidata links, and
“Today we’re excited to launch multimodal capabilities in LlamaCloud, which gives you the full toolkit to build e2e multimodal RAG pipelines across any unstructured data in minutes – whether it’s over marketing slide decks, legal/insurance contracts, finance reports. All you have
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