Context Engineering

Most AI projects don’t fail at the model. They fail somewhere between the demo that impressed everyone and the system that has to survive contact with a real customer’s data, workflows, and definitions. I published a new LinkedIn Learning course on the role that closes that gap: Forward Deployed Engineering in the Age of AI. Why This Course Exists AI deployment differs fundamentally from a traditional SaaS rollout. A SaaS product is largely the same on day one for every customer. An AI system is not — its behavior depends on the customer’s data quality, their vocabulary, their edge cases, and their tolerance for being wrong. That means the work of making it succeed happens in the field, not at headquarters. ...

Originally published on Kepler Everyone’s talking about context engineering right now, but most of the conversation is focused on the wrong thing. Read the blog posts, the guides, the thought leadership. They’re all asking the same questions: What should I include in the context window? How do I manage tokens efficiently? How do I curate what the model sees? These are valid questions. They’re also the easy part. The hard part isn’t deciding what context to include. It’s building systems that deliver that context reliably, with provenance, at scale, every single time. That’s not a context problem. That’s an engineering problem. And engineering means something specific. ...