Retrieval-Augmented Generation Is An Engineering Problem, Not A Model Problem

In practice, retrieval is a system with its own failure modes, its own latency budget and its own quality requirements.

The retrieval rebuild: Why hybrid retrieval intent tripled as enterprise RAG programs hit the scale wall

Enterprise intent to adopt hybrid retrieval tripled from 10.3% to 33.3% in Q1 as first-gen RAG architecture failed at agentic ...
InfoQ

Google Vertex AI Provides RAG Engine for Large Language Model Grounding

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Dany Lepage discusses the architectural ...

Databricks research shows multi-step agents consistently outperform single-turn RAG when answers span databases and documents

Data teams building AI agents keep running into the same failure mode. Questions that require joining structured data with unstructured content, sales figures alongside customer reviews or citation ...
Search Engine Land

RAG: The most important AI tool marketers have never heard of

To date, much of the early conversation about putting AI into production at scale has centered on the need for good prompt engineering — the ability to ask the right questions of this powerful ...
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How RAG Boosts Agency Accuracy and Security

Alexander Slagg is a freelance writer specializing in technology and education. He is an ongoing contributor to the CDW family of magazines. Agencies awash in oceans of data might seem like an ideal ...
Semiconductor Engineering

RAG-Enabled AI Stops Hallucinations, Adds Sources

Many EDA companies have taken the first steps to incorporate generative AI into their tools, and in such tightly controlled environments GenAI appears to have great benefits. But its broader adoption ...