Prompt engineering was one of the earliest skills developers learned when using large language models.
Today, I think another discipline is quietly becoming more important:
Context engineering.
Most AI coding tools already understand syntax, APIs, and programming languages remarkably well.
Where they struggle is understanding your project.
Questions like these are surprisingly difficult for an AI to answer consistently:
- Why was this design chosen?
- Which architectural decisions are intentional?
- What assumptions are still true?
- Which old decisions should be revisited?
These aren’t code problems.
They’re context problems.
The more I work with AI-assisted development, the more I believe that persistent project context will become foundational infrastructure.
Instead of rebuilding context in every conversation, projects need a structured memory that both humans and AI can understand.
That’s one of the motivations behind Contorium.
The goal isn’t to replace developers or create another autonomous coding agent.
It’s to preserve project knowledge in a way that survives across tools, IDEs, and AI sessions.
Code generation will continue improving.
But I suspect the next competitive advantage won’t just be who generates better code.
It will be who understands the project better before writing a single line.
That’s where I think context engineering begins.
https://www.contorium.dev/
https://github.com/ContoriumLabs/contorium
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