Context Engineering focuses on the discipline of designing inputs AI systems use to reason, retrieve, and respond. The emphasis stays on structure and signal quality. You learn how to shape context windows, data objects, instructions, and constraints so outputs stay accurate, grounded, and useful. Topics include context layering, intent framing, retrieval boundaries, failure modes, evaluation loops, and production patterns across analytics, content, and operations. Built for founders, executives, and senior operators who want consistent AI behavior without brittle prompts.
I wired an AI system into a real flower shop to see why so many AI projects collapse under real constraints. This article shows how Model Context Protocol fixed AI decisions by grounding an LLM in intent, behavior, inventory, and revenue instead of prompts and theory.
