For the last year, everyone has been talking about one architecture.
RAG.
Retrieval-Augmented Generation.
Need your AI to answer questions?
Add a vector database.
For the last year, everyone has been talking about one architecture. RAG. Retrieval-Augmented...
RAG fails on context, not retrieval—AI needs code, logs, APIs, Git history, memory orchestrated intelligently to reason reliably. Teams must shift from optimizing vector databases to managing diverse context sources; context engineering drives competitive AI advantage.
For the last year, everyone has been talking about one architecture.
RAG.
Retrieval-Augmented Generation.
Need your AI to answer questions?
Add a vector database.

The narrative that RAG is dead has been repeated by enough credible voices that many engineering leaders have started to believe…

RAG sounds complicated. It's not. But a lot of introductions to RAG make it sound more mysterious...

RAG is not new. Chunk a document, embed the chunks, store them in a vector database, run a retrieval...

The vector database category is undergoing a shift in response to the needs of agentic AI.

Retrieval-augmented generation is the most requested AI feature and the most commonly botched. The...

Learn how context retrieval works in AI agents, why basic RAG fails at scale, and how Redis supports reliable retrieval with…