Context Is the Moat. Not the Model. Most teams building AI products are optimizing the wrong thing. They debate model size, chase benchmark scores, and spend weeks on fine-tuning runs. Then they ship something that feels hollow. Generic. Like a customer support bot that clearly has no idea who it’s talking to. I’ve seen this…
Data Freshness Rot: The Silent Killer of Production RAG Systems Most ML engineers I know are obsessed with model quality. Better evals, better prompts, better fine-tuning. And honestly, I get it. Model quality matters. But after watching enough production RAG systems quietly degrade over months, I’m convinced the real killer isn’t the model at all….
Data Freshness Rot: The Silent Failure Mode Killing Your RAG System Everyone I know building AI systems is obsessed with model quality. Better evals, tighter prompts, more sophisticated retrieval. And that obsession is understandable. Model quality is visible. You can benchmark it. You can show stakeholders a chart. But the thing quietly destroying production RAG…
The Silent Killer in Your RAG Pipeline Most ML engineers I know are obsessed with model quality. Better evals, better prompts, more fine-tuning. I get it. Model quality matters. But the thing quietly destroying production AI systems right now is not a model problem at all. It is data freshness rot. And almost nobody treats…
Data Freshness Rot: The Silent Killer of Production RAG Systems Most ML engineers I know are obsessed with model quality. Better evals. Smarter prompts. More fine-tuning. And I get it, that stuff matters. But after years of building and debugging production AI systems, I’ve come to believe the thing quietly destroying most RAG deployments has…
