Vector databases? Overrated relics. PageIndex just hit 98.7% accuracy on brutal financial QA benchmarks by reasoning smarter, not searching dumber.
92% of enterprise AI teams plan RAG overhauls by 2026. Here's the no-BS breakdown of what actually works.
Servers groaning under billions of embeddings. That's where vector databases swoop in—or do they? I've seen this rodeo before.
Your AI assistant just invented a new tax law. Again. RAG promises to stop the nonsense by feeding it real-time facts. But let's see if it's the savior or another tech mirage.
We all bought the hype: cram more tokens, problem solved. Wrong. Memory demands ruthless systems design, or your agentic dreams crash into stateless oblivion.
AI startups love their shiny vector stores. But when real money hits, they crumble without SQL's iron grip.
Cursor slashed search costs by 95% overnight with a scrappy side project. Now Turbopuffer's founder is promising to return investor cash if it flops by December—talk about skin in the game.
Hundreds of research papers have piled onto RAG since its 2020 debut. But after two decades watching Valley hype cycles, I'm asking: does this actually fix LLMs, or just kick the can?
AI agents forget without memory. Vectors deliver fuzzy recall fast; graphs trace exact paths. Here's the market math on choosing right.