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retrieval-augmented-generation

A collection of 2 issues

Nimble's Web Search Agents claim 51% token cuts, but the methodology gap matters

Nimble has released Web Search Agents, a retrieval system the company says delivers 21% better accuracy and 51% lower token usage compared with leading AI search alternatives. The company frames the product as a solution to a specific enterprise AI problem: general-purpose search APIs return too many irrelevant results,

AI agents fail at data engineering, not the context layer

AI agents fail in production not because of bad context but because of stale, unvalidated data upstream. Teams chasing better retrieval architectures or context-layer vendor products treat the symptom while missing the underlying data engineering problem. Retrieval pipelines score relevance or availability, not correctness, so stale or missing data