From Vibe Coding Hangovers to Sustainable AI-Assisted Development

I’ll be honest: the first time I let Claude Code generate an entire feature implementation, I felt both excited and skeptical. Watching hundreds of lines of well-structured code materialize in seconds was intoxicating. I merged that feature, moved on to the next task, and felt incredibly productive.

Then came the hangover.

Later, when I needed to modify that feature, I realized I didn’t truly understand the code I’d merged. Edge cases I hadn’t considered were lurking. The initial high of “vibe coding”, where AI generates large blocks of code with minimal guidance, had worn off. This left me with technical debt and a maintenance burden.

If you’re an engineering leader exploring AI-assisted development, you’ve likely heard similar stories. The promise is real: AI can dramatically accelerate development cycles and help teams tackle more ambitious projects. But the gap between promise and sustainable practice is where most teams stumble.

After integrating Claude Code into my iOS development workflow, I’ve learned that the key isn’t avoiding AI assistance; it’s leveraging it effectively. It’s using it strategically. Here’s what actually works.

The Vibe Coding Trap: When Speed Becomes Liability

Vibe coding is seductive. You describe a feature in natural language, AI generates the implementation, you glance at it, think “looks reasonable,” and commit. Dopamine hits. Velocity metrics climb. Everyone’s happy.

Until they’re not.

I experienced this firsthand while building a tabbed navigation component with custom animations. The first implementation of AI-generated code looked impressive. Smooth transitions, proper state management, all the features I’d requested. I made a few tweaks, got it working, and moved on.

Then came the refinement requests. “Can we adjust the animation curve?” “The tab indicator isn’t quite centered.” “We need haptic feedback on selection.”

Each iteration, AI would regenerate substantial portions of the code. And each time, subtle regressions would creep in: animation timing that was previously correct would break, edge cases we’d already handled would resurface, state management that worked would suddenly introduce race conditions.

After a certain number of iterations, more things broke than worked. I’d spent more time debugging AI-generated regressions than I would have spent building the feature manually from the start. The promised productivity gains had evaporated.

The core problem wasn’t the AI; it was my approach. I was treating AI like a code generator instead of a development assistant. I was optimizing for speed of initial implementation while ignoring the total cost of ownership.

Review Fatigue: The Hidden Cognitive Tax

Even when vibe coding works initially, it creates another challenge: review fatigue.

When AI generates 200 lines of code in seconds, you’re suddenly responsible for thoroughly reviewing every line. Is the error handling correct? Are there race conditions? Does it follow your architectural patterns? Is it following established team norms? Is it actually solving the right problem?

This cognitive load is substantial. In traditional development, you write code incrementally, thinking through each decision as you go. Your brain naturally chunks the complexity. With AI-generated code, you’re handed all of the complexity at once and expected to validate it comprehensively.

I’ve caught myself doing cursory reviews. I would scan for obvious issues, but I lacked the mental energy for deep scrutiny simply because reviewing hundreds of AI-generated lines multiple times per day is taxing. This is exactly when the code becomes less familiar, and bugs slip through. The sense of accomplishment from completing a manually written feature is replaced by a fleeting satisfaction with AI-generated code you don’t fully understand.

The Sustainable Approach: AI as Your Enthusiastic Pair Programmer

After struggling with these challenges, I’ve adopted a different mental model: AI isn’t a code generator; it’s an enthusiastic pair programmer who never gets tired.

This shift changed everything about how I structure my work.

1. Manual Coding with AI as Built-In Stack Overflow

For complex business logic and critical features, I write the code myself but with AI immediately available for consultation and review.

Need specific help with the proper way to handle Swift 6 concurrency with actors? Ask AI. Unsure about the right SwiftUI modifier combination? AI will have helpful suggestions. Forgotten the syntax for a complex generic constraint? AI’s got it.

This gives you a sustained, productive buzz without the crash. You’re learning and retaining knowledge because you’re actively engaged in writing the code. But you’re not wasting time context-switching to Google or Stack Overflow every few minutes. Claude Code is working within your project directory and is giving advice specific to the project.

The code you write this way is yours. You understand it deeply because you made every decision. When you return to modify it weeks later, you’ll remember the context and trade-offs.

2. Structure Your Requests Efficiently

When you do ask AI to generate code, vague requests waste both tokens and time. Both the speed and token efficiency of AI’s response are directly proportional to the clarity and specificity of your request.

Vague request:

Please fix the existing build error

Structured request:

Please fix the following build error on line 19 of `MealListView.swift`,
"Reference to member 'mealListSearchPrompts' cannot be resolved without a contextual type"

The second approach gives AI clear context and directions. AI will not need to perform a build and then scan the build output for the error. The resulting code will be generated more quickly and more efficiently with token usage.

For engineering leaders: This efficiency requirement has team implications. Developers need to develop skills in prompt and context engineering: clearly articulating requirements, constraints, and architectural context. This is a learnable skill that pays dividends in output quality, token efficiency, and speed.

3. Trust But Verify: The Non-Negotiable Review Process

Every line of AI-generated code must be reviewed with the same rigor you’d apply to a developer’s pull request.

I enforce these checks:

• Does it actually compile? AI sometimes generates code that looks plausible but has subtle syntax errors.
• Does it follow our architectural patterns? If you’re using MVVM, does the code properly separate concerns?
• Is error handling comprehensive? AI often generates “happy path” code that doesn’t gracefully handle failures.
• Are there concurrency issues? With Swift 6, this means verifying proper actor isolation and Sendable conformance.
• Does it include tests? AI can generate tests too, insist on it.

This review process is where you learn. You’re not just validating code; you’re understanding decisions, spotting patterns, and building intuition about what AI does well and where it struggles.

4. Leverage Specialized Agents

Claude Code offers specialized agents optimized for specific tasks. This is where the tool really shines.

Instead of asking the general-purpose model to “review my code for issues,” I use a custom review agent specifically designed to catch Swift 6 concurrency violations, actor isolation issues, and modern iOS best practices.

The agent’s output is focused and actionable because it’s not trying to be everything to everyone—it’s specialized for the exact task I need.

For your team: Identify which specialized agents align with your stack and team needs. Train developers to route requests to the appropriate agent rather than using the general model for everything. The quality improvement is significant.

5. Custom Commands: Automate Your Patterns

Claude Code supports custom slash commands. These are predefined prompts tailored to your specific workflows and architectural patterns.

I created a /scaffold command for my iOS projects that generates the complete Clean Architecture structure for a new feature: View, ViewModel, Use Case, Repository, and corresponding test files. They are all following my exact naming conventions and architectural patterns.

// Running /scaffold GeneratesOrderHistory feature creates:

UI/OrderHistory/
  OrderHistoryView.swift          // SwiftUI view
  OrderHistoryViewModel.swift     // @MainActor ViewModel with State/Action

Domain/UseCases/
  OrderHistoryUseCase.swift       // Protocol + Default implementation

Data/Repositories/
  OrderHistoryRepository.swift    // Protocol + Default implementation

Tests/
  OrderHistoryViewModelTests.swift
  DefaultOrderHistoryUseCaseTests.swift
  DefaultOrderHistoryRepositoryTests.swift

This scaffolding is generated in seconds and perfectly adheres to my architectural conventions. No more inconsistent file structures or naming variations across features.

For engineering leaders: Custom commands are force multipliers for architectural consistency. Define commands for your team’s common patterns, and suddenly everyone’s code looks cohesive regardless of experience level.

The Hybrid Approach: Scaffolding + Manual Implementation

After months of experimentation, I’ve settled on this workflow for complex features:

1. Use AI for scaffolding: Generate the file structure, protocol definitions, basic types, and test stubs using custom commands or structured requests.

2. Manually implement the critical logic: Write the business logic, state management, and algorithmic complexity and corresponding tests yourself. This is where you need deep understanding and careful decision-making.

3. Review everything: Run the entire diff through the review agent and manually apply any suggestions. Then do one final manual code review.

This hybrid approach gives you the best of both worlds:

• Speed from AI handling boilerplate and scaffolding
• Understanding from manually writing complex logic
• Consistency from AI following your documented patterns
• Quality from careful human review and decision-making

Team Adoption Considerations

If you’re considering introducing Claude Code, or any AI, to your team, be thoughtful about the rollout.

Start with non-critical projects. Let developers build intuition about where AI helps and where it struggles without risking production stability.

Establish review standards. AI-generated code shouldn’t bypass your normal code review process. If anything, it should receive more scrutiny initially until patterns emerge.

Share learning in retrospectives. When someone discovers an effective prompt pattern or identifies a failure mode, capture that knowledge for the team.

Watch for over-reliance. If developers stop being able to explain their code because “AI wrote it,” you have a problem. The goal is augmentation, not replacement.

The Verdict: Sustainable Productivity Requires Strategy

AI-assisted development is transformative when used strategically.

The vibe coding approach gives you a temporary high but leaves you with code you don’t understand and a mounting maintenance burden. The hangover is real.

The sustainable approach treats AI as a tireless collaborator: always available to answer questions, generate boilerplate, and handle tedious scaffolding, but never replacing the critical thinking and architectural decision-making that defines quality software.

For engineering leaders, the opportunity is significant: faster iteration cycles, more consistent codebases, and developers who spend more time on interesting problems and less time on boilerplate. But realizing this opportunity requires intentionality about how AI tools integrate into your team’s workflow.

Start small. Learn what works for your team and codebase. Build expertise in structuring effective requests. Establish rigorous review processes. Create custom commands that encode your architectural patterns.

Most importantly, remember that the goal isn’t to replace engineering skill—it’s to amplify it. The best results come when experienced developers leverage AI to handle the tedious parts of software development, freeing them to focus on the creative, architectural, and strategic challenges that still require human judgment.

This isn’t about shortcuts, it’s about sustainable productivity.

Ready to experiment? Pick one small, low-risk project. Try the scaffolding, then the manual approach. Review everything carefully. Share what you learn. And remember: you’re looking for a sustained buzz, not a spike-and-crash.

At Atomic Robot, we believe in sustainable development because software investments live and grow across years, not weeks. If this resonates with you, we’re here to help you plan how to adopt AI into your development process.

Photo by Katja Anokhina on Unsplash