Most AI coding projects follow the same pattern: promising start, then complete breakdown as complexity grows.
As a Principal Software Engineer, I’ve realized the issue isn’t the AI, it’s that we abandoned basic software engineering principles when AI assistants arrived.
We wouldn’t code without requirements docs or architecture plans, but with AI we type „build me a todo app“ and expect production-ready results.
The problem: Treating AI like magic instead of applying systematic development practices.
The solution: Context Architecture, structured JSON documents that provide AI with comprehensive, machine-readable context (like how we use schemas for databases).
This manifesto argues for treating context as infrastructure, not chat history. The methodology applies proven engineering principles to AI development: structured planning, version-controlled context docs, and systematic processes that scale.
Core insight: 80% planning through context architecture, 20% execution through AI coding.
Anyone else noticed this same failure pattern? Curious what approaches have worked for maintaining consistency in larger AI-assisted projects.
submitted by /u/Cgvas
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