Traditional AI interaction follows a request-response pattern: you ask, the AI answers, and then you start over. This often creates what I call "context amnesia"—where every interaction begins from scratch, forcing you to rebuild understanding each time.
It's like having a conversation with someone who forgets everything you've discussed the moment you stop talking.
A great context workflow flips this dynamic. The core premise is designing systems where context accumulates, roles remain consistent, and the AI becomes a persistent and effortless collaborator across your projects.
Context Engineering in Practice: My Devin Workflow
I've been an early adopter of Devin by Cognition Labs, and through extensive use, I've developed a highly effective "vibe coding" workflow. One of my first projects, Contextify, was even designed to make context management transparent in AI chats, allowing users to easily toggle context on and off.
Here's a look at my streamlined collaboration with Devin:
My Workflow Structure
Persistent Context: Devin consistently understands my codebase, project goals, and design philosophy across all our sessions.
Clear Roles: I concentrate on product vision, user experience, and testing, while Devin expertly handles bug fixes, feature implementation, and complex technical problem-solving.
Human Oversight: Critical decisions and user-facing changes always undergo human review and judgment.
Task Delegation Framework
- Large Features: Firebase Studio
- Small Features: Devin
- Design UX/UI: Devin
- Project Management: Linear
- Code Review: Devin/Gemini
- Debugging: Devin/Gemini
We leverage Linear and Git workflows, along with clear documentation, to maintain continuous context. Every new code change is submitted to GitHub as a pull request.
The first step in mastering context workflows is understanding what elements need to be automated, what should remain manual, and where human-in-the-loop intervention is crucial. This isn't a one-size-fits-all decision—it depends on the type of work, the stakes involved, and the expertise required.
The Automation Layer: What AI Should Handle
Pattern Recognition and Application
AI excels at identifying patterns and applying established frameworks. In code development, this means recognizing architectural patterns, applying coding standards, and implementing well-defined features. In content creation, it means maintaining style consistency, following format guidelines, and applying established templates.
Information Processing and Synthesis
AI can rapidly process large amounts of information and synthesize insights. This includes analyzing data patterns, summarizing research, cross-referencing documentation, and identifying relevant precedents or examples.
Routine Implementation Tasks
Once the strategic direction is clear, AI can handle the detailed implementation work—writing code, formatting documents, creating variations on established themes, and maintaining consistency across large projects.
The Manual Layer: What Humans Should Control
Strategic Decision Making
Humans should maintain control over project direction, user experience priorities, business logic, and any decisions that affect stakeholders. AI can provide analysis and recommendations, but humans make the final calls on strategy.
Quality Standards and Judgment
While AI can follow guidelines, humans define what quality means in context. This includes aesthetic judgment, user experience evaluation, ethical considerations, and determining when something is "good enough" versus when it needs refinement.
“Humans should handle communication that requires empathy, negotiation, or relationship management. AI can draft communications, but humans should review and send anything that affects relationships or commitments.
Starting Small: The Context Workflow Bootstrap
Don't try to implement perfect context workflows immediately. Start with one specific type of work and gradually expand the pattern:
Four Steps to Start
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Choose a Repetitive Task: Pick something you do regularly that involves similar patterns—code reviews, content creation, data analysis, or research synthesis.
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Define the Context Template: Create a simple structure that captures the essential context for this type of work. What background information does the AI need to be successful? What are the quality standards? What decisions require human input? What tools will you use in the process and when?
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Establish the Handoff Pattern: Design clear transition points between human and AI work. When do you provide input? When do you review output? How do you maintain continuity?
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Iterate on the Workflow: Pay attention to friction points and context breakdowns. Refine the workflow based on what actually works, not what you think should work.
Scaling Context: From Tasks to Projects
Once you've mastered context workflows for individual tasks, you can scale the approach to entire projects:
Project Context Architecture: Design information architecture that supports persistent context across multiple work streams. This might include shared documentation, consistent naming conventions, and clear role definitions.
Cross-Session Continuity: Develop practices that maintain context across work sessions. This could involve session summaries, context handoff documents, or structured project state tracking.
Multi-AI Coordination: As you work with multiple AI systems, design workflows that maintain context consistency across tools. Each AI should understand its role in the broader project context.
Towards Effortless Intelligence
Effortless Intelligence represents a fundamental shift in how we think about human-AI collaboration. Instead of humans adapting to AI limitations, we're designing systems where AI adapts to human thinking patterns and work preferences.
“Organizations that master context workflows will have significant advantages in speed, quality, and innovation capacity. They'll be able to tackle more complex problems, explore more creative solutions, and maintain higher standards while moving faster.
Adaptive Workflows: Design workflows that can adjust based on project complexity, timeline constraints, or team composition. Context workflows should be flexible, not rigid.
This has implications beyond individual productivity. The transition won't happen overnight, but the early indicators are clear.
See It in Action
The effortless intelligence approach becomes visible when work feels effortless despite its complexity.
In Contextify development, when I can focus entirely on user experience decisions while Devin handles technical implementation seamlessly, that's context workflow mastery in action.
When project continuity is maintained across sessions without extensive setup time, when AI outputs consistently meet quality standards without micromanagement, and when human review feels strategic rather than corrective—these are the indicators that context workflows are functioning as designed.
I'm a designer and technologist who believes the future of work lies in thoughtful human-AI collaboration rather than human-AI competition. This exploration of context workflows emerges from practical experience building AI-powered systems and working extensively with AI development partners. The goal isn't to optimize AI performance, but to design collaboration patterns that amplify human capability and creativity through intelligent context management.
