Pillar 1: Work Abstraction
The first skill in context workflow mastery is learning to abstract work at the right level. Too granular, and you're micromanaging the AI. Too abstract, and you lose control over quality and direction.
I've found that the sweet spot lies in what I call "intent-level abstraction." Instead of specifying implementation details, you define the desired outcome and the constraints that matter.
Intent-Level Abstraction in Practice
Instead of: "Create a React component with useState for managing form data, use Material-UI for styling, add validation for email format, and make sure the submit button is disabled until all fields are valid..."
Try: "Build a user registration form that follows our design system and prevents invalid submissions. The user should feel confident about data quality before they can submit."
The AI can handle the technical implementation details, but you maintain control over user experience and business logic.
This level of abstraction lets you focus on what humans do best—understanding user needs and making strategic decisions—while letting AI handle what it does best—translating requirements into working code.
Common Prompting Error
One common error with asking ChatGPT for help prompting a product requirement document or build plan is that ChatGPT will generally include technical details that are not compliant with an existing codebase. When prompting, ask ChatGPT for instructions in layman terms with no technical details.
Pillar 2: Collaborative Tool Integration
Working effectively with AI tools requires understanding how to structure collaboration for maximum efficiency. Here are the patterns I've developed:
Incremental Validation
Build in regular checkpoints where you can review progress and provide course corrections. AI systems work best with frequent feedback loops rather than long periods of autonomous work.
Role-Based Boundaries
Clearly define what decisions the AI can make independently and what requires human input. For technical implementation, Devin has broad autonomy (mostly guided by our codebase). For user experience decisions or business logic changes, I maintain control.
“Documentation isn't just for future reference—it's a way to maintain shared context. When the AI documents its decisions and reasoning, it creates context that persists across sessions.
Version Control as Communication
Git workflows become a communication channel. Commit messages, pull requests, and code reviews create a shared language for discussing changes and maintaining project continuity.
Pillar 3: Human Review
The most important element of any context workflow is the human review process. This isn't about checking the AI's work for errors—it's about maintaining strategic control over outcomes while leveraging AI capabilities.
Effective human review operates at multiple levels:
Strategic Review
Does this work align with our broader goals? Are we solving the right problem? This is uniquely human territory—AI can optimize for specified objectives, but humans define what objectives matter.
Quality Review
Does this meet our standards for user experience, code quality, and maintainability? AI can follow patterns and best practices, but humans understand the subtle trade-offs that determine long-term success.
Context Review
Is the AI maintaining appropriate context, or is it drifting from our established patterns? This is where you catch and correct context degradation before it compounds.
Learning Review
What can we learn from this collaboration to improve future workflows? Human review isn't just about the current work—it's about continuously refining the collaboration patterns.
The Key Insight
Human review should feel natural and efficient, not like a bottleneck. When context workflows are well-designed, review becomes a strategic activity rather than a quality control burden.
What This Means for Technologists
The three pillars represent a fundamental shift in how we think about human-AI collaboration. We're not just using AI as a tool—we're designing collaboration systems that amplify human capability.
The next million AI users won't just be prompt engineers. They'll be people leveraging disciplines they haven't been formally trained in—therapy, coaching, teaching, engineering, biology, science—with machine assistance for onboarding into these domains.
The challenge for designers and technologists is clear: How do we scale beyond chat and generative interfaces while maintaining good design principles? How do we create systems where the human+machine intelligence threshold unlocks capabilities neither could achieve alone?
These pillars are a starting point, not a conclusion. I'd love to hear how you're thinking about structuring your own human-AI workflows.
