Rag

The most expensive part of your AI agent stack might not be what you think. While developers obsess over model selection and prompt engineering, retrieval is quietly eating your latency budget and your inference bill — and most production RAG pipelines are using general-purpose LLMs for a specialized task they weren’t built for. Chroma’s new Context-1 model is a direct challenge to that pattern. It’s a 20-billion-parameter open-source retrieval model that outperforms GPT-5 on HotpotQA and FRAMES benchmarks while running 10 times faster and costing 25 times less per query. Released on HuggingFace under an open license, it’s purpose-built for one thing: getting the right information out of large corpora for RAG pipelines and agent memory workflows. ...

A ZDNET survey of chief data officers finds that 50% of organizations deploying agentic AI cite data quality and retrieval issues as their primary barrier. Executives are responding by increasing data management investment specifically to unblock agent deployments — not as a general data hygiene initiative, but as a direct prerequisite for getting agents into production. If you’re in that 50%, here’s a practical framework for what to actually fix. ...

Weaviate just shipped something that’s going to change how AI coding agents handle data: Agent Skills, an open-source repository of pre-built capabilities that give Claude Code, Cursor, GitHub Copilot, VS Code, and Gemini CLI native access to semantic search, vector storage, and multi-agent orchestration blueprints. What Are Agent Skills? Agent Skills are modular, ready-to-use capability bundles that plug directly into AI coding agents. Rather than writing custom tool integrations every time you want an agent to search a vector database or store semantic embeddings, you drop in the relevant Agent Skill and it just works. ...