Worried about sending sensitive documents to the cloud? Two research releases show you can get competitive accuracy while keeping data local. On Nov 3, 2025 Trove shipped as an open-source retrieval toolkit that cuts memory use 2.6× and adds live filtering, dataset transforms, hard-negative mining, and multi-node runs. On Nov 13, 2025 a local hybrid RAG system combined semantic embeddings and keyword search to answer legal, scientific, and conversational queries entirely on device. Why it matters: privacy, latency, and cost trade-offs now favor hybrid and on‐device retrieval for regulated customers and production deployments. Immediate moves: integrate hybrid retrieval early, vet vector DBs for privacy/latency/hybrid support, use Trove-style evaluation and hard negatives, and build internal pipelines for domain tests. Outlook: ~80% confidence RAG becomes central to AI stacks in the next 12 months.

Over the past 14 days OpenAI rolled out new API updates: text-embedding-3-small and text-embedding-3-large (small is 5× cheaper than prior generation and improved MIRACL from 31.4% to 44.0%; large scores 54.9%), a GPT-4 Turbo preview (gpt-4-0125-preview) fixing non‐English UTF‐8 bugs and improving code completion, an upgraded GPT-3.5 Turbo (gpt-3.5-turbo-0125) with better format adherence and encoding fixes plus input pricing down 50% and output pricing down 25%, and a consolidated moderation model (text-moderation-007). These changes lower retrieval and inference costs, improve multilingual and long-context handling for RAG and global products, and tighten moderation pipelines; OpenAI reports 70% of GPT-4 API requests have moved to GPT-4 Turbo. Near term: expect GA rollout of GPT-4 Turbo with vision in coming months and close monitoring of benchmarks, adoption, and embedding dimension trade‐offs.