Image created with gemini-2.5-flash-image with claude-sonnet-4-5-20250929. Image prompt: A cinematic photograph of a stately English law library with towering mahogany bookshelves, an ornate lectern in the foreground displaying an illuminated manuscript, and luminous golden threads connecting the open text to specific glowing legal volumes on the shelves, warm candlelight creating a reverent scholarly atmosphere that evokes both classical tradition and the precise retrieval of knowledge.
Agentic RAG takes RAG beyond “retrieve then answer.” With AI agents that choose tools, adapt strategies, and critique outputs, it’s the smarter way to build LLM apps. https://x.com/n8n_io/status/1963927630043807862
This is one of the most promising directions to improve RAG systems. It involves combining dynamic retrieval with structured knowledge. It helps to mitigate hallucinations and outdated information, and improves knowledge quality. Pay attention to this one, AI devs! https://x.com/omarsar0/status/1967963949158240485
Your RAG pipeline isn’t performing well? The problem might not be your 𝘳𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭 or 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘰𝘯 – it’s probably your data preparation. Most people jump straight into building RAG systems without properly preparing their data. But here’s the thing: https://x.com/femke_plantinga/status/1968691549358686357
