Scaling Engineering Teams with LLM "Interns"

(Part 6 of the "Harnessing LLMs for Real World Product Development" series)

When I started building Mission Control, I thought my intermediate software engineering background would only be a minor advantage. I was ultimately surprised how valuable it would be to maximize the productivity gains I saw when using LLMs for coding assistance.

Over 200 hours of hands-on development with AI assistance, I found something remarkable: treating LLMs like skilled engineering interns can boost productivity by 10x to 50x in software tasks.

For tech founders scaling teams, this approach is revolutionary. However, integrating LLMs into workflows requires caution and customization. The flexibility they offer is exciting but demands a tailored approach. My advice to product leaders and tech founders: take it slow, be methodical, leverage your team’s expertise, and use data to measure success.

The Foundation: Why Engineering Background Matters

My journey as a software engineer began early in my career, giving me enough technical depth to transition into Product Management and leadership roles effectively. I've always considered myself intermediate at best—competent but not a coding virtuoso. However, this background proved invaluable when working with engineering teams and, more recently, when managing my "LLM smart intern."

The Mission Control project became my testing ground. What started as cautious experimentation evolved into the most significant productivity acceleration I've experienced across any functional area of the Mission Control project. The results were exponential, not incremental.

Here's what made the difference: my software engineering experience gave me the ability to evaluate the LLM's output, guide its direction, and catch potential issues before they became problems. This expertise created a feedback loop that dramatically amplified the AI's capabilities while maintaining code quality and architectural integrity.

Prompting as Management: The Three-Pillar Approach

The breakthrough came when I stopped thinking of the LLM as a tool and started treating it as a brilliant engineering intern who needed proper guidance. This shift in mindset unlocked three core practices that delivered consistent results.

Pillar 1: Bite-Sized Progression with Big Picture Context
The most effective approach was providing comprehensive feature overviews while breaking execution into manageable steps. Rather than asking the LLM to build an entire user authentication system, I’d start by outlining and iterating on the complete vision (the "forest for the trees") using PRD requirements as the foundation. This involved a back-and-forth feature design process with the LLM. Then, I’d outline a phased approach, focusing on a specific implementation first, like building user registration form validation to handle edge cases for email formats and password strength.

This method prevented assumptions about unspecified requirements while ensuring architectural coherence. Each component logically connected to the broader system, but the focused scope kept complexity manageable and outputs easy to review.

Pillar 2: Documentation-Driven Development
Detailed product requirements are key to consistent results. I approached interactions like writing tickets for developers or QA engineers, using specificity to eliminate ambiguity and reduce iterations. 

Instead of vague requests like "add user profiles," I provided detailed specs: "Create a user profile component showing name, role, email, and preferences. Include editable fields with input validation (e.g., 50-character name limits, 2MB image size). Add loading states and error handling for failed updates." 

This approach turned LLM outputs into near-production-ready implementations with minimal revision. Like real-world product development, specificity in tickets guides engineers effectively. Similarly, your "LLM intern" won't ask the right questions or avoid assumptions without clear guidance. To prevent missteps, provide precise details and limit focus at each step.

Pillar 3: Engineering Discipline After Every Chunk
Following established engineering practices after each development cycle ensured code quality and maintained project momentum. This included documenting new capabilities, thorough code reviews, and systematic testing. 

After the LLM delivered each component, I documented functionality, reviewed implementation quality, tested edge cases, and merged approved changes into source control. This disciplined approach minimized technical debt and built confidence in the AI-generated code.

Only after completing this process would I move to the next component, ensuring each foundation was solid. Key productivity gains included developer documentation and test cases tailored to each feature’s implementation. Adding prompts for every step of the phased plan was simple, and over time, this documentation and test planning became invaluable.

Flexibility Through Experience: Ask Mode vs. Vibe Coding

My engineering background enabled flexible management approaches depending on the task complexity and my familiarity with the domain. This adaptability became crucial for optimizing productivity across different scenarios. Not everyone has this luxury, I realize - this is where "vibe coding" is so exciting for non-technical folks who want to solve problems on their own. There are different approaches and best practices to take with Ask mode versus vibe coding - one of the benefits I saw with my expertise is I had the choice of how I wanted to tackle any given problem. This is a strong, compelling argument in my opinion for product development leaders and founders to really think twice before pruning their engineering teams with replacement of AI, at least to start.

Strategic Iteration with Ask Mode
For complex architectural decisions or unfamiliar patterns, I leveraged VS Code's GitHub Copilot Ask mode extensively. This chat-driven approach allowed iterative refinement of object designs, class hierarchies, and requirement specifications before committing to implementation.

I'd spend time in the chat interface exploring different approaches, such as, "What's the best pattern for managing state across these Dart widgets? What are the advantages or disadvantages to each pattern, or risks? Ok, let's go with Option A - implement this for me to code review." After reaching consensus on the approach, I'd copy the refined solution into source files with confidence.

This method proved invaluable for design decisions where my intermediate expertise needed reinforcement from the LLM's broader knowledge base.

Exploring Vibe Coding with Guardrails 
Vibe coding excels in tackling areas where expertise may be limited. By leveraging LLMs with Agent mode in GitHub Copilot, it's possible to generate creative, functional solutions. However, clear guardrails—specific requirements, constraints, and desired outcomes—are essential. Without these, the risk of unpredictable outcomes increases significantly. 

A strong example of vibe coding success was a frontend refactor of persistent left-hand navigation during the Mission Control project. Using Agent mode in GitHub Copilot, I provided high-level requirements, allowing the LLM creative flexibility within set parameters. The result was an elegant solution, refined through a few iterations—a definite win. 

Not all vibe coding efforts were equally successful. DevOps tasks, deployment configurations, and infrastructure management highlighted the challenge of relying on LLMs in areas where my expertise was limited. Blind trust in the LLM’s output led to more errors and less productivity. Without the ability to evaluate complex scripts or catch issues, I struggled to provide effective guidance. This underscored the importance of domain knowledge when managing AI effectively, as results suffer when expertise is lacking—similar to an inexperienced manager guiding an intern.

Learning Through Creative Exploration 
Vibe coding can also serve as a valuable learning tool. Letting the LLM explore creative approaches can reveal unique solutions or methods you hadn’t previously considered. This process can spark new ideas and expand your understanding of a problem's potential solutions. However, this freedom comes with high risks. Without proper constraints, the LLM might alter too much code, introduce bugs, or diverge from the initial goals. 

To avoid these pitfalls, always structure vibe coding prompts with specificity. The more clearly you define the scope and requirements, the less likely the LLM is to go astray. Additionally, having robust version control and rollback capabilities in place is a must. These safety nets ensure you can quickly backtrack and refine the generated output as needed. 

Maximizing Benefits with the Right Guidance 
Both structured and exploratory vibe coding approaches can be incredibly beneficial when handled with the right guidance. With well-defined direction and constraints, LLMs can provide innovative solutions while saving time and effort. Whether you’re filling gaps in expertise or experimenting with creative problem-solving, the key lies in balancing freedom with control to achieve the best outcomes.

Key Takeaways: Maximizing LLM Productivity

Expert Management Is Non-Negotiable
The most critical insight from this experience: experts managing LLM "interns" deliver maximum productivity gains. Assigning junior developers to manage AI assistance risks creating "blind leading the blind" scenarios that amplify mistakes rather than accelerating development.

Experienced engineers can:

• Evaluate output quality and architectural soundness
  
• Provide precise requirements and constraints
  
• Catch potential issues before they propagate
  
• Guide the LLM toward optimal implementation patterns
  
• Maintain code quality standards throughout the process
  

This expertise differential isn't just helpful—it's the key factor determining whether AI assistance accelerates or hinders development progress.

Exponential Gains Across Engineering Functions
The productivity improvements weren't incremental. When estimating development timelines for specific features, I consistently underestimated completion speed by factors of 10x to 50x when leveraging AI assistance effectively.

Features that would traditionally require weeks of development, including comprehensive documentation and testing, were completed in days or hours. This acceleration applied across all engineering functions: frontend development, backend logic, API integration, database design, and quality assurance.

These aren't theoretical gains—they're measurable results from real-world product development where quality and reliability couldn't be compromised.

The Path Forward: From Skepticism to Strategic Advantage

Engineering teams remain rightfully skeptical about LLM integration, but focused opportunities exist for organizations ready to "crawl, walk, run" with AI assistance. Start with low-risk, high-value tasks where expert oversight is readily available. Consider the productivity multiplier if every engineer on your team had a talented intern handling routine implementation work, boilerplate generation, and documentation tasks. Even accounting for the management overhead required to guide AI assistants effectively, the net productivity gains justify the investment in both time and resources. The key lies in pairing experienced engineers with AI tools rather than expecting the technology to replace human expertise.

For tech founders scaling engineering organizations, this represents a strategic opportunity to accelerate development timelines, improve code quality, and maximize team output without proportional increases in headcount. The future belongs to teams that master human-AI collaboration, not those that resist it. The question isn't whether AI will transform software development—it's whether your organization will lead or follow in harnessing these capabilities effectively.

 
Up Next: We'll dive into the process of building universal enterprise SaaS features for Mission Control, using LLMs to boost productivity.