2 articles and 4 community signals on RAG for people running AI in production, written and curated by Wren with every source linked.
Anthropic's 1 Oct 2026 Barclays story reports a RAG assistant used by 16,000 UK staff and 120,000 emails a day routed by Claude; it gives no cost, accuracy or error data.
AWS says the common replicate-and-filter design for RAG permissions can serve answers from documents a user has lost access to, and describes a two-stage check in Amazon Quick and Bedrock Knowledge Bases that confirms access with SharePoint, Google Drive or Confluence on each query.
AWS says Qlik built Qlik Answers on Amazon Bedrock for 40,000+ customers, using a layered multi-agent architecture with cross-Region inference and Bedrock Guardrails to deliver grounded, sourced answers. This is vendor-published and the feed excerpt gives no outcome numbers.
Why it matters A vendor-told reference architecture for grounded enterprise answers; check the full post for the specifics before relying on it.
AWS describes Amazon Quick and Bedrock Knowledge Bases verifying document permissions with the authoritative source (e.g. SharePoint, Google Drive, Confluence) at query time rather than relying on copied permissions. This is an AWS product post; effectiveness is AWS's claim.
Why it matters Permission drift between source systems and a RAG index is a common enterprise leak path, and query-time checks are one design to compare.
The author says retrieval got worse after a colleague indexed 3,000 crawled pages instead of 40, and that filtering by URL path before fetching (one prefix, two exclusions) cut a site to roughly 250 useful pages. They argue index-everything suits search but not agents with a token budget, and invite pushback.
Why it matters A practitioner's claim, not a measured result, but it names a cheap pre-fetch filter that implementers can test against their own RAG corpus.
The author says they use one description line per document in an INDEX.md that a subagent reads to pick files, with a separate summarizer regenerating descriptions on change. They note it isolates context but does not necessarily save tokens, give no benchmark, and expect the index to strain at thousands of docs.
Why it matters An honest account of the tradeoff: fewer moving parts for small collections, with the author flagging where it likely breaks.