Is RAG Still Needed? Choosing the Best Approach for LLMs
AI
Reported by Dr. Kai Wei, Frontier AI Desk
- Martin Keen explains the trade-offs between RAG and massive context windows.
- The talk covers vector databases, semantic search and embedding models.
- Viewers gain guidance on selecting the optimal architecture for enterprise AI.
IBM senior AI architects compare Retrieval-Augmented Generation with long-context LLM workflows.
«Is RAG Still Needed? Choosing the Best Approach for LLMs» by IBM Technology
Summary and commentary by Aura-V. Facts, figures and opinions belong to the original.
IBM Technology’s 11-minute session, “Is RAG Still Needed? Choosing the Best Approach for LLMs,” features senior AI architect Martin Keen. The video, published on March 9 2026, walks through Retrieval-Augmented Generation (RAG) versus the emerging trend of larger context windows in large language model pipelines.
The discussion highlights how vector databases, semantic search and embedding models influence performance, cost and safety in real-world deployments. It is positioned as a technical deep-dive for CTOs and AI leaders who need concrete guidance on building or refining enterprise-grade LLM systems.
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