Large language models now support context windows up to 1-2M tokens, making RAG unnecessary for many use cases. For datasets under 500K tokens with moderate query volumes, directly injecting the entire corpus into the prompt is simpler, cheaper with context caching, and often more accurate than traditional RAG pipelines. The
Table of contents
Table of ContentsContext Windows in 2026: Where We Actually AreRAG in 60 Seconds (And Where It Still Wins)Long Context Injection: The "Just Stuff It In" ApproachHead-to-Head: Long Context vs. RAG on Five DimensionsThe Vector DB vs. Token Cost CalculatorThe Hybrid Architecture: Best of BothPractical Implementation GuideWhat This Means for the Vector Database MarketChoose Boring Architecture (Until You Can't)Sort: