This week
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Updated through the day. Every headline links straight to the source. The two lines underneath are ours.
This is a concrete, reproducible fix for one of RAG's oldest failure modes: chunks losing meaning when stripped from their surrounding document. Anthropic reports meaningful retrieval accuracy gains when combining contextual embeddings with contextual BM25 and reranking. If you run a production RAG pipeline, this is worth testing against your own eval set before you reach for a bigger model or a pricier vector database.
This is the closest thing to a reference architecture for production GenAI systems that a builder will find outside a vendor's own documentation, and it's vendor-neutral. Use it as a checklist against your own stack: if you're missing guardrails or context augmentation, you're probably further behind than you think. Worth bookmarking for any team scoping a new AI product.
This became the reference architecture that most agent frameworks since have implicitly copied: planner, memory module, tool use loop. If you're building or evaluating an agent stack today, this is still the clearest mental model to check your design against, even two years later. The gap it exposes early, that reflection and long-term memory are the hard parts, is still the gap most agent products haven't closed.