Context Engineering, Not Bigger Models, May Be the Key to Better AI Coding Tools

A developer who has extensively used AI coding assistants like Claude, ChatGPT, and Cursor argues that the primary bottleneck in AI-assisted development is not model intelligence but context quality. Current workflows often consume thousands of tokens just setting up a problem — pasting files, explaining structures, and detailing past attempts — before the model begins solving anything. Larger context windows, such as one-million-token limits, do not fully address this inefficiency, as feeding an entire codebase to an AI is analogous to asking a developer to read every file before fixing a minor bug. The proposed solution, called context engineering, involves automatically identifying and supplying only the files, functions, and dependencies relevant to the specific task at hand. The author believes future AI developer tools will differentiate themselves not by which underlying model they use, but by how precisely and intelligently they construct context for each query.