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In real-world product development, delivering and maintaining a high-quality product is a universal goal. There are various ways teams approach QA, all of which are important. Some rely on developer-led unit tests, others use dedicated QA teams for functional testing, and many involve other functions like PMs or CX to validate against requirements. The most robust strategy incorporates all three, ensuring comprehensive quality coverage.However, resource constraints often force difficult choices. The Mission Control project, a solo endeavor completed within a 200-hour timebox, presented a significant QA challenge. With no QA team to rely on, I used LLMs to amplify my effectiveness. Luckily, I have some background in QA Engineering, which helped "manage" my QA "AI intern!" <blockquote>This post details the tangible productivity gains realized by integrating LLMs into the QA process, offering a few points of advice for founders and development teams looking to accelerate their validation efforts.</blockquote><h3>Automating QA-Focused Documentation </h3>One of the most immediate and impactful applications of LLMs is in generating developer and QA documentation, which, let's be honest, is not something many teams excel at anyway! So why not use LLMs to generate solid technical and testing documentation as low-hanging fruit? Throughout the Mission Control project, I established a workflow where the LLM was responsible for maintaining technical documentation for every feature. This included implementation approaches, key architectural decisions, and identified risks.As part of this process, I prompted the LLM to write detailed manual test cases for each new capability. The model excelled at outlining the various paths and permutations a user might take, creating comprehensive test suites in seconds. A task that would typically take me 30 to 60 minutes to document was completed almost instantly. At the end of each development phase, I had a complete set of developer documentation and a corresponding suite of test cases ready for execution. This simple practice delivered significant time savings and ensured no quality detail was overlooked. Plus, in some cases where the LLM was doing more of the coding heavy lifting, having documentation of its approach, the decisions we made up front in technical design, along with solid, focused test cases, was a benefit to simply help me remember how certain systems had been implemented over time.<h3>Building In-App Runtime Testing and Debug Capabilities </h3>One of the best decisions I made early on in the Mission Control project was to build a dedicated debug page within the application itself. This powerful tool, accessible only to admin and super-admin roles, became the command center for all runtime testing and validation. It provided direct access to test core functionalities, saving countless hours that would have been spent on manual setup.The debug page enabled streamlined testing of several key areas:<ul><li>User and Company Management: I could instantly create users, build company profiles, and manage affiliations, simulating complex enterprise structures without a lengthy setup process. </li><li>Content and Data Management: The page allowed for the rapid creation and manipulation of content, AI-generated smart tags, and the raw data structures tracked by the application. </li></ul>This internal tool made validating specific test cases incredibly efficient. Simulating scenarios that would normally require significant prep time became a matter of a few clicks. The productivity boost was immense, blending LLM-powered feature development with practical internal testing needs. For teams new to LLM integration, building sequestered, non-production tools like this is an excellent, low-risk way to begin leveraging AI for development and QA.<h3>Accelerating Data Validation with Test Scripts </h3>The third major area where LLMs delivered exceptional value was in data validation. As users interact with an application, ensuring the integrity of the data their actions generate is critical. Mission Control used both PostgreSQL for structured data and Firestore for its document-based database, creating a complex validation challenge.I found LLMs to be incredibly proficient at writing Python scripts for this purpose. Within minutes, I could generate a series of scripts to perform data integrity checks on new tables, validate data structures in Postgres, and test new service endpoints. This process, which would have taken me hours to complete manually, was reduced to a few minutes of iterating with the LLM to produce a robust test script.The productivity gain here was exponential. I estimate a 25-50x increase in efficiency compared to writing these scripts from scratch. This allowed me to maintain a high level of confidence in the application's data integrity throughout the rapid development cycle.<h3>Conclusion: LLMs Significantly Boost QA Efficiency </h3>Integrating LLMs into the QA workflow for Mission Control yielded productivity gains ranging from 10x to 50x across documentation, in-app testing, and data validation. The results speak for themselves: leveraging AI for quality assurance is not just a theoretical concept but a practical strategy that delivers tangible benefits.For any founder or team looking to accelerate their development lifecycle, I highly recommend exploring how LLMs can enhance your QA processes. The key to success is placing your most experienced QA experts in charge of guiding and managing these AI-powered efforts. An LLM is a powerful tool, but its effectiveness is magnified by the expertise of the person directing it.In the next post in this series, we will summarize and grade the productivity gains achieved across the entire Mission Control project, providing a comprehensive verdict on the real-world impact of harnessing LLMs in product development.

Boosting QA Productivity with LLMs: Lessons from Mission Control

Harnessing LLMs for Real-World Product Development: Post 8 <h3>Introduction </h3>Throughout the Mission Control project—a 200-hour challenge to build a production-ready app solo using LLMs—there were several occasions where the AI went completely off the rails. These experiences were invaluable, teaching me how to recognize when a project was heading for trouble and how to recover effectively when failures occurred.This post shares the key lessons learned from those challenges. For any founder or developer integrating LLMs into their product development workflow, these insights can help you avoid common pitfalls, manage your AI "intern" more effectively, and execute a swift recovery when things go wrong.<h3>Early Warning Signs: Preventing Failure Before It Starts </h3>Recognizing the early indicators of an impending AI failure is the most effective strategy for mitigating risk. Here are a few crucial warning signs I identified and the preventative measures that proved most effective.1. An Inadequate Development Environment One of the first and most critical recommendations is the importance of a professional-grade development environment (which is why this was one of the "north star" production standards I set up front). Attempting to manage AI-generated code without robust safeguards is a recipe for disaster. The most essential preventative tool is source control.For Mission Control, I used a combination of <a data-mce-href="https://github.com/" href="https://github.com/" title="GitHub" target="_blank" rel="noopener">GitHub</a> and its integration with <a data-mce-href="https://code.visualstudio.com/" href="https://code.visualstudio.com/" title="VS Code" target="_blank" rel="noopener">Visual Studio Code</a>. This setup, paired with <a data-mce-href="https://github.com/features/copilot" href="https://github.com/features/copilot" title="GitHub Copilot" target="_blank" rel="noopener">GitHub Copilot</a>, provided the control needed to manage code changes with confidence. Knowing I could discard file changes or roll back to a previous commit at any time offered peace of mind. It created a safety net that allowed me to experiment with the LLM's suggestions without risking the entire project.2. Working in Low-Data Domains LLMs are only as knowledgeable as their training data. If you are working in a niche technical area, using a newer technology, or tackling a domain with sparse documentation, you must temper your expectations. The LLM will not be as reliable or accurate.I encountered this twice with Mission Control:<ul><li>DevOps in Google Cloud: The world of DevOps can be esoteric, with much of the knowledge siloed in the minds of experienced professionals rather than in clear, accessible documentation. When using an LLM to navigate <a data-mce-href="https://cloud.google.com/" href="https://cloud.google.com/" title="Google Cloud" target="_blank" rel="noopener">Google Cloud's</a> infrastructure, I frequently ran in circles dealing with user permissions, security settings, and role configurations. My own lack of deep expertise in this area compounded the problem, making it difficult to guide the LLM toward a correct solution. </li><li>Directus CMS: I intentionally chose <a data-mce-href="https://directus.io/" href="https://directus.io/" title="Directus" target="_blank" rel="noopener">Directus</a>, an emerging headless CMS, for its powerful capabilities and cost-effectiveness. However, its newer status meant less training data for the LLM. With less public community support and a smaller documentation footprint, the productivity boost from the AI was noticeably diminished. </li></ul>Knowing you are entering a less-supported domain allows you to adjust your strategy. Rely less on the LLM for flawless execution and more for brainstorming and foundational code, while preparing for more manual oversight.3. Giving the LLM Too Much Leeway I quickly learned to recognize a "spidey sense" that things were about to go wrong, which almost always happened when I got greedy with my requests. Treating the LLM as a talented but inexperienced intern is the correct mental model. You wouldn't ask an intern to build a complex feature from scratch without supervision; the same principle applies here.The most effective preventative measure was a structured, phased approach to feature development:<ul><li>Collaborative Design: First, I worked with the LLM in a conversational manner to outline a technical design and implementation strategy for the new feature. </li><li>Phased Implementation: Once the design was solid, I broke the project into small, manageable phases. For each phase, I provided a highly specific prompt detailing the exact requirements and explicitly stating that no other code should be changed. </li><li>Validation and Documentation: Each prompt concluded with a directive to stop development and await manual testing. I also required the LLM to document its changes against my technical design and provide test cases for me to execute. </li></ul>This bite-sized approach was the single most effective technique for preventing major failures and keeping the project on track.<h3>Failure Recovery: Pivoting and Rebuilding </h3>Despite the best preventative measures, failures will still happen. When they do, having a clear recovery plan is essential.1. When the LLM Gets Stuck: Pivot Your Approach There were instances where, even with a phased approach, the LLM simply could not resolve a bug or implement a feature correctly. The typical pattern was this: I'd complete a phase, but the feature would have persistent bugs that the LLM was unable to fix. At its most frustrating, the LLM would start repeating its previous, ineffective recommendations—a sure sign you're in a pointless loop.One memorable example was a refactoring project to make the left-hand navigation in Mission Control static. I got most of the way there, but a gnarly bug caused the navigation to revert to its old behavior during certain page transitions. The LLM was stumped.To break the cycle, I pivoted my approach entirely. Instead of asking for a fix, I instructed the LLM to write a detailed technical document outlining the code path during the problematic page transition. Using this LLM-generated documentation, I manually debugged the code. The process quickly uncovered the root cause: the LLM was confusing two similarly named functions and occasionally calling the old, incorrect one. This was a "head-scratcher" moment that reinforced the "talented intern" model—the AI lacked the higher-level awareness to see its own mistake. By changing the task from "fix this" to "explain this," I found the solution.2. Recovering from Catastrophic Failures Of course, there were times when the LLM went completely off the rails, leading to catastrophic code changes. These are the scenarios that can be most harmful to a project if the right safeguards aren't in place.Early in the development of Mission Control, I got ambitious and tasked the LLM with implementing content obfuscation based on a user's company—a core feature for a B2B SaaS application. I should have broken this down, but instead, I asked for a major change to the object model to associate users with a new "company" entity. The LLM made incorrect, sweeping changes that corrupted some of the user data.This was an anxiety-inducing moment, but recovery was manageable for two reasons:<ul><li>Source Control: Rolling back the code to its last known good state was simple thanks to my GitHub setup. </li><li>Test Data: The corrupted data was minimal and only for testing purposes. However, it was a stark reminder of the potential damage in a production environment. </li></ul>After reverting the code and manually fixing the data, I re-approached the feature with the LLM using a much more granular, phased strategy. I eventually succeeded, but the experience underscored the absolute necessity of source control, developer documentation, and clear test cases.<h3>Conclusion </h3>The 200-hour Mission Control project taught me so much in terms of leveraging LLMs for real-world development. In particular, how to prevent failures, pivot when an LLM gets stuck, and recover from catastrophic errors. It was a learning process conducted in a controlled environment, and the insights gained have proven invaluable.For anyone integrating LLMs into their development process, I hope these lessons provide tangible value. By treating your LLM as a capable intern and implementing the right safeguards, you can avoid major pitfalls and harness its power to accelerate your product development.In my next post, I will explore how LLMs can be used to boost productivity and effectiveness in the QA process.

When AI Fails: How to Avoid Pitfalls and Recover

Welcome to the seventh post in our series, "Harnessing LLMs for Real-World Product Development." The central question for my "Mission Control" project was this: Can LLMs truly deliver value in real-world development scenarios while adhering to the stringent production standards that define professional software?This post will detail how I maintained those standards while building the universal features every product team encounters. I'll break down my approach, the specific actions I took, and the critical lessons learned. The results confirm that, with the right strategy, LLMs can be powerful accelerators, not just novelties.<h3>Defining "Real-World" Production Standards </h3>To test the viability of LLMs, I established a non-negotiable set of production-grade standards from day one. These weren't watered-down requirements; they were the same benchmarks any professional team would set for a public-facing product.My core commitments were:<ol><li>Infrastructure Excellence: The system had to be scalable, performant, and reliable. No compromises. </li><li>Modern Technology Stack: I selected a stack that offered flexibility for future development and extensibility. </li><li>Universal Capabilities: The application needed to include common B2B SaaS features to simulate authentic development challenges. </li><li>Workflow Best Practices: I followed modern software development lifecycle (SDLC) best practices, from source control to DevOps security practices.</li></ol>These standards formed the foundation of the Mission Control challenge. The goal was not just to build an app but to build it right.<h3>How I Applied Production Standards with LLMs </h3>Adhering to my standards required a deliberate and methodical approach. It involved more than just asking an LLM to write code. I needed to integrate the model into a professional workflow, treating it as a specialized intern that required precise direction and rigorous oversight.Data and Asset Load Performance Performance was a critical metric. My architectural decisions, which included a <a data-mce-href="https://hasura.io/" href="https://hasura.io/" title="Hasura" target="_blank" rel="noopener">Hasura</a> GraphQL layer over a <a data-mce-href="https://www.postgresql.org/" href="https://www.postgresql.org/" title="postgreSQL" target="_blank" rel="noopener">PostgreSQL</a> database and asset storage in <a data-mce-href="https://cloud.google.com/" href="https://cloud.google.com/" title="Google Cloud" target="_blank" rel="noopener">Google Cloud Storage</a>, were only part of the solution. The implementation had to be efficient.I developed a highly specific prompting strategy to guide the LLM in constructing performant data queries and asset retrieval calls. For example, when fetching data, I instructed the model to design GraphQL queries that minimized data transfer and optimized for speed. Similarly, for image assets, the prompts specified efficient loading practices from Google Cloud Storage. Every integration built with the LLM was subjected to performance validation to ensure it met my speed and efficiency benchmarks.I ended up refactoring certain capabilities because the load times were not up to par with my requirements. For example, the AI-generated smart tagging feature of content in Mission Control was not performant after the first implementation. Content would load, but the smart tagging module in the dashboard took seconds to load. I went through an iterative refactoring process within <a data-mce-href="https://code.visualstudio.com/" href="https://code.visualstudio.com/" title="VS Code" target="_blank" rel="noopener">VS Code</a>'s Copilot for GitHub integration, following these steps:<ol><li>I provided a high-level expectation of load time performance for the Smart Tags Widget to the LLM, with quantifiable times expected (less than 0.5 seconds). I had the LLM write a technical proposal for the smart tagging capability that covered how it planned on reducing load times and what the pros and cons would be of these different options. </li><li>I made a decision and defined a phased refactoring project to limit the scope of changes at any given time. I directed the LLM to implement the changes phase by phase, pausing to allow me to manually verify those changes were not straying from the big-picture goal.</li><li>Ultimately, the refactoring reduced the load time of the smart tag module from ~5 seconds to &lt;0.5 seconds, a successful outcome.</li></ol>Building Universal B2B SaaS FeaturesThe Mission Control app was designed as a testbed for building features common to most enterprise software. This allowed me to simulate the challenges a typical product team faces. With the LLM's assistance, I successfully designed and built:<ul><li>User Authentication &amp; Management: Standard create account, login, and user profile management with preference settings. </li><li>Content Management: A hybrid CMS driven by both Directus for structured content and a PostgreSQL/GCP Storage combination for dynamic data and assets. </li><li>Metadata-Based Search: Functionality to search content based on associated metadata. </li><li>Collaboration Tools: Features like team notes, user-to-user requests, and event scheduling to facilitate teamwork. </li><li>Data Obfuscation: Backend systems designed to enforce strict data separation between different customers—a cornerstone of SaaS architecture. </li></ul>For each feature, I provided the LLM with strict requirements grounded in B2B SaaS expectations. The prompts demanded solutions that were performant, reliable, and delivered a high-quality user experience. Each of these features required an approved technical proposal, a phased project definition, and an iterative workflow with the LLM "intern" building out changes per phase, then pausing and documenting those changes and writing test case recommendations that I could then test manually to sign off on. This workflow worked successfully because it was methodical, well-planned, and followed real-world product development best practices (strong, clear requirements, iterative design decisions, solid documentation, and testing).Adhering to Modern Development Workflows Building a product is as much about process as it is about code. I integrated the LLM into established, modern workflows.<ul><li>DevOps and Permissions: I configured my Google Cloud Platform environment with standard user and system roles, restricting access based on the principle of least privilege. This area proved challenging. Without prior DevOps experience, I was in the "blind leading the blind" scenario I've discussed before. While the LLM helped me complete the tasks, the productivity gains were modest due to the iterative and cautious approach required. But with virtually no DevOps experience, I was still able to get the job done with LLM assistance. </li><li>Containerization: I used Docker to containerize my backend services, including the PostgreSQL database, Hasura API layer, and the Flutter application itself. This ensured consistency across development and deployment environments. </li><li>Source Control: I used GitHub in VS Code from the very beginning. This was an absolute necessity. It provided a safety net, allowing me to experiment with LLM-generated solutions and easily roll back changes that didn't meet my standards. </li><li>Robust Debugging: Like any professional development effort, I built a comprehensive debug panel into the app. It was hidden behind a feature flag and tied to my debug configuration, allowing me to test user profiles, generate test data (using LLMs), modify company attributes, and validate my AI integrations like smart tagging and search. </li></ul>Some additional benefits I realized along the way with an LLM "intern" are the ease with which I could generate solid developer documentation and test cases, which proved extremely valuable. I'll write further about the QA benefits I saw from LLM assistance in a future post.<h3>Key Learnings and Observations </h3>The Mission Control project proved that it is entirely possible to build a product to real-world standards using LLMs as a core part of the development team. However, success depends on a strategic shift in how you manage the work.We observed exponential productivity gains—in some cases, between 10x and 50x—across most software engineering tasks. The notable exception was in specialized areas like DevOps, where a lack of foundational knowledge limited the LLM's effectiveness as a force multiplier.Ultimately, we built Mission Control: a functional simulation of a real-world SaaS platform built with the same rigor as a commercial product. This experiment validated our hypothesis that LLMs can be integrated into professional workflows to accelerate development without sacrificing quality.Success, however, isn't automatic. It requires a different type of oversight than managing human engineers. It hinges on deliberate planning, clear and explicit prompting, robust testing and validation protocols, and leveraging the LLM to create thorough documentation for future reference. When you treat the LLM as a highly capable but literal-minded team member, you unlock its true potential to build better products, faster.Next up: We'll delve into best practices around recovery situations when AI fails, and any preventative measures that are must-haves to put into place before diving into development.&nbsp;

Upholding Production Standards with LLMs

(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.<blockquote>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.</blockquote> 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.<h3>The Foundation: Why Engineering Background Matters </h3>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.<h3>Prompting as Management: The Three-Pillar Approach </h3>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.&nbsp;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."&nbsp;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.&nbsp;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.<h3>Flexibility Through Experience: Ask Mode vs. Vibe Coding </h3>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 <a data-mce-href="https://code.visualstudio.com/docs/copilot/overview" href="https://code.visualstudio.com/docs/copilot/overview" title="VS Code GitHub Copilot" target="_blank" rel="noopener">VS Code's GitHub Copilot</a> 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&nbsp; 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.&nbsp;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.&nbsp;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&nbsp; 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.&nbsp;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.&nbsp;Maximizing Benefits with the Right Guidance&nbsp; 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.<h3>Key Takeaways: Maximizing LLM Productivity </h3>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:<ul><li>Evaluate output quality and architectural soundness </li><li>Provide precise requirements and constraints </li><li>Catch potential issues before they propagate </li><li>Guide the LLM toward optimal implementation patterns </li><li>Maintain code quality standards throughout the process </li></ul>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.<h3>The Path Forward: From Skepticism to Strategic Advantage </h3>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.&nbsp; Up Next: We'll dive into the process of building universal enterprise SaaS features for Mission Control, using LLMs to boost productivity.

Scaling Engineering Teams with LLM "Interns"

This article continues our series on harnessing LLMs for real-world product development through our Mission Control project. As we've established throughout this series, building production-grade software demands more than flashy demos—it requires deliberate architectural decisions that stand up to real-world demands.Two of our four North Star strategic goals directly shaped every architectural choice we made.&nbsp;<ul><li style="">First, we needed an architecture that delivers Reliability, Performance, and Scalability from day one.&nbsp; </li><li style="">Second, our tech stack had to enable Flexibility and Extensibility as our product evolves.&nbsp;</li></ul>These weren't abstract principles—they were concrete requirements that guided every decision from backend infrastructure to frontend frameworks.<blockquote>The stakes were clear: if Mission Control couldn't demonstrate these capabilities, it would fail as a genuine test of real-world product development. Our architectural planning process needed to prove that LLMs could accelerate not just code generation, but the strategic thinking that creates lasting technical foundations.</blockquote><h3>The Decision Framework: From Breadth to Depth </h3>Our methodology followed the same decision framework we've refined throughout this series: systematic "shootouts" between multiple LLM models using identical prompts to research architectural options. This wasn't random experimentation—it was structured evaluation designed to surface the best solutions quickly.The process began with broad research prompts sent simultaneously to <a data-mce-href="https://claude.ai/" href="https://claude.ai/" title="Claude AI" target="_blank" rel="noopener">Claude</a>, <a data-mce-href="https://chatgpt.com/" href="https://chatgpt.com/" title="ChatGPT AI" target="_blank" rel="noopener">ChatGPT</a>, <a data-mce-href="https://copilot.microsoft.com/" href="https://copilot.microsoft.com/" title="Microsoft CoPilot" target="_blank" rel="noopener">CoPilot</a>, and <a data-mce-href="https://www.perplexity.ai/" href="https://www.perplexity.ai/" title="Perplexity AI" target="_blank" rel="noopener">Perplexity</a>. Each LLM received the same context: our product requirements, performance expectations, and scalability needs. The goal was maximum coverage of potential solutions in minimum time.From there, we moved through an iterative breadth-to-depth process. We narrowed prompts to focus on the most promising options while eliminating LLMs that consistently delivered weaker responses. This natural selection approach quickly identified which models excelled at architectural analysis versus surface-level suggestions.The framework ultimately narrowed to two primary LLM chat threads conducting deep research across architectural options. Each generated detailed decision documents that became our evaluation foundation. These documents included five critical components:<ul><li>Technology Capability Assessment: What each component could actually do, not marketing promises but real-world functionality based on documentation and case studies. </li><li>Mission Control Alignment: How each option supported our specific product requirements, from user authentication to content management to B2B SaaS capabilities. </li><li>Multi-Criteria Analysis: Pros and cons evaluated against selection criteria including costs, extensibility, performance, robustness of capabilities, and integration complexity. </li><li>Risk Assessment: Potential failure points and mitigation strategies, because every architectural decision carries technical debt implications. </li><li>Community and Documentation Evaluation: The strength of community support and quality of documentation—a factor that would prove crucial for LLM productivity gains later in development. </li></ul>This systematic approach transformed what could have been weeks of research into days of focused analysis. The LLMs didn't just accelerate information gathering—they structured it for better decision-making.<h3>Critical Architecture Decisions </h3>Our research process required decisions across five major architectural domains: backend infrastructure, database technology, authentication systems, service layer architecture, and frontend frameworks. Each decision interconnected with the others, creating a web of technical dependencies that demanded careful consideration.<ul><li>Backend Infrastructure: Evaluated AWS, GCP/Firebase, and Azure, focusing on startup needs like limited budgets, small teams, rapid iteration, and future scalability.&nbsp; </li><li>Database Decisions: Considered relational, document, and hybrid databases, as well as deployment options like managed services, self-hosted, and containerized solutions. Modeled scenarios from launch to scaling with LLMs.&nbsp; </li><li>Authentication and User Management: Explored built-in solutions, third-party services, and hybrid approaches to balance robust user experiences with strong security.&nbsp; </li><li>Service Layer: Assessed REST APIs, GraphQL, and hybrid options to weigh performance, flexibility, and development complexity.&nbsp; </li><li>Frontend Frameworks: Balanced short-term productivity with long-term goals, including potential expansion to mobile, desktop, and new platforms. </li></ul>Throughout this process, our third North Star goal remained paramount: we needed to build something that demonstrated Real-World, Universal Capabilities. Every product team faces the same core requirements—user authentication, profile management, roles and permissions, content management, search and tagging, and B2B SaaS features like customer data segregation.Our tech stack choices had to support all these capabilities without creating architectural bottlenecks. This wasn't about building the minimum viable product—it was about creating a foundation that could evolve from MVP to enterprise-grade platform.<h3>The Winning Tech Stack </h3>After extensive research and evaluation, our analysis yielded a specific technology combination: GCP/Firebase for backend infrastructure, containerized PostgreSQL for the database, Directus as our headless CMS, Hasura for the API layer, and Flutter for the frontend framework.These choices weren't made in isolation—each component strengthened the others while addressing our strategic goals.<a data-mce-href="https://cloud.google.com/" href="https://cloud.google.com/" title="Google Cloud Home" target="_blank" rel="noopener">Google Cloud</a> / <a data-mce-href="https://firebase.google.com/" href="https://firebase.google.com/" title="Firebase Home" target="_blank" rel="noopener">Firebase</a> emerged as our backend infrastructure winner for several compelling reasons. The Firebase ecosystem provides cost-effective document-based data management with authentication and user management built in. Google Analytics 4 integration gives us powerful analytics capabilities without additional complexity. Storage capabilities for assets and messaging systems come standard.The cost structure perfectly aligns with startup realities—essentially free until significant traction develops, then backed by GCP's enterprise-grade scalability for future growth. The usability factor proved decisive; Firebase enables rapid development without sacrificing professional capabilities. Google's backing ensures long-term viability, while robust documentation and community support maximize LLM productivity gains.<a data-mce-href="https://www.postgresql.org/" href="https://www.postgresql.org/" title="PostgreSQL" target="_blank" rel="noopener">PostgreSQL</a> represented the safe, scalable choice for our primary database. As a battle-tested relational database with excellent performance characteristics and virtually unlimited scalability, PostgreSQL offers the reliability our first North Star goal demanded. The extensive community support and documentation also aligned with our LLM productivity requirements.<a data-mce-href="https://directus.io/" href="https://directus.io/" title="Direcus Home" target="_blank" rel="noopener">Directus</a> solved our headless CMS needs with robust content management capabilities and cost-effective pricing. Among all CMS technologies we evaluated, Directus offered the best balance of functionality and affordability. Its integration capabilities with Firebase/GCP and Flutter sealed the decision, enabling us to demonstrate a complete content pipeline within Mission Control.<a data-mce-href="https://hasura.io/" href="https://hasura.io/" title="Hasura Home" target="_blank" rel="noopener">Hasura</a> provided our API layer solution with strong PostgreSQL and Firebase integration. The GraphQL flexibility it offers enables efficient frontend queries while maintaining backend performance. Hasura's approach to API generation dramatically reduces boilerplate code while providing the extensibility our second North Star goal required.<a data-mce-href="https://flutter.dev/" href="https://flutter.dev/" title="Flutter Development Home" target="_blank" rel="noopener">Flutter</a> won our frontend framework evaluation as a cross-platform solution that enables immediate web app development while preserving mobile expansion options. Flutter's growing ecosystem, strong Firebase/GCP integration, and compatibility with our other tech stack components made it the clear choice. The ability to reach iOS, Android, and web platforms from a single codebase offers exactly the flexibility and extensibility we needed.<h3>The Community Support Advantage </h3>One crucial insight emerged from our architectural planning process: when building systems you plan to enhance with LLM intelligence, choose well-supported, well-documented technologies. This principle became a key selection criterion that influenced every decision.LLMs are only as capable as the training data they've learned from. Technologies with extensive documentation, active communities, and widespread adoption generate more training examples, leading to better LLM responses and higher productivity gains during development.This insight proved prescient during actual development. Throughout the Mission Control build process, I received more consistent and substantive assistance from LLMs when working with Firebase, PostgreSQL, Hasura, and Flutter. These technologies have extensive documentation and active communities, resulting in training data that enables LLMs to provide detailed, accurate, and contextually appropriate guidance.Directus presented the exception that proved the rule. As the least supported component in our tech stack, Directus consistently generated weaker LLM responses. Documentation gaps and smaller community adoption meant fewer high-quality examples for LLMs to learn from. This translated to more trial-and-error development, more debugging time, and lower overall productivity gains.The lesson is clear: architectural decisions should factor in LLM ecosystem support as a legitimate selection criterion. In an AI-accelerated development world, choosing technologies that maximize LLM effectiveness becomes a competitive advantage.<h3>Results and Recommendations </h3>The process of using LLMs (large language models) to assist in architectural planning and tech stack selection delivered impressive results. By accelerating research and decision-making, we achieved significant productivity gains and created a strong, scalable foundation for development. Here are the key recommendations based on our experience:<ul><li>Accelerate research and evaluation: Use LLMs to speed up initial research, compare options, analyze criteria, and make informed choices faster.&nbsp; </li><li>Structure decision-making: Adopt a systematic framework to evaluate risks, integration challenges, and scalability considerations upfront rather than encountering surprises later.&nbsp; </li><li>Leverage LLMs as partners, not decision-makers: Use LLMs to gather data, structure comparisons, and highlight trade-offs, but keep final strategic decisions aligned with your specific goals in human hands.&nbsp; </li><li>Build for scalability and adaptability: Prioritize architectural decisions that support long-term growth, adaptability, and extensibility under real-world pressures.&nbsp; </li><li>Focus on foundations: A solid tech stack and well-thought-out architecture make everything built on top more reliable, scalable, and successful. </li></ul>By combining the speed and insights of AI with human strategic judgment, teams can make better architectural decisions in less time. This approach not only accelerates development but also ensures the foundation is strong enough to support future growth and innovation.Next up: we'll dive into how LLM assistance boosted the productivity of the development process and what we learned along the way to maximize results and avoid rollbacks.&nbsp;

How AI Boosted the Architectural Design Process for Mission Control

This article is part of our ongoing series on harnessing LLMs to deliver real-world value in product development. Through the Mission Control project, we aim to provide a transparent view of how AI accelerates high-performing product teams. My core strengths are in product management, leadership, and some engineering, but UX design has always been an area where I’ve relied on seasoned partners to turn vision into meaningful user experiences.With Mission Control, I intentionally worked solo—a deliberate test of how much productivity could be achieved by leveraging an LLM as a dedicated UX design assistant. <blockquote>The objective was clear: could AI efficiently bridge my expertise gap and deliver a production-ready design?</blockquote><h3>The AI Design Shootout: From Prompt to Prototype </h3>Every product design project needs a solid foundation—the product requirements document (PRD). Generated with the support of <a data-mce-href="https://www.chatprd.ai/" href="https://www.chatprd.ai/" title="ChatPRD AI" target="_blank" rel="noopener">ChatPRD</a>, our PRD captured strategic statements on the core problem, solution, vision, high-level requirements, and essential features. This served as our single source of truth for what needed to be built.With the requirements in place, I kicked off a “shootout” among leading AI tools. I fed the PRD into <a data-mce-href="https://claude.ai/" href="https://claude.ai/" title="Claude AI" target="_blank" rel="noopener">Claude</a>, <a data-mce-href="https://copilot.microsoft.com/" href="https://copilot.microsoft.com/" title="Microsoft CoPilot" target="_blank" rel="noopener">Microsoft CoPilot</a>, <a data-mce-href="https://chatgpt.com/" href="https://chatgpt.com/" title="ChatGPT" target="_blank" rel="noopener">ChatGPT</a>, and <a data-mce-href="https://www.figma.com/ux-design-tool/" href="https://www.figma.com/ux-design-tool/" title="Figma UX Design" target="_blank" rel="noopener">Figma’s</a> AI-driven prototyping features, asking each to generate a visual concept for the Mission Control dashboard. This let me objectively compare how each platform translated complex product needs into coherent, actionable interfaces.<h3>Iteration and Refinement: Finding the Winning Workflow </h3>The initial concepts from each platform were starting points. Real progress began with focused, iterative cycles—review, critique, revised prompts, and new outputs. The differences in output quality and practical utility quickly became clear.Through this process, I narrowed my toolkit to <a data-mce-href="https://claude.ai/" href="https://claude.ai/" title="Claude AI" target="_blank" rel="noopener">Claude</a> and <a data-mce-href="https://www.figma.com/ux-design-tool/" href="https://www.figma.com/ux-design-tool/" title="Figma UX Design Tool" target="_blank" rel="noopener">Figma</a>.Claude consistently generated mockups with visual impact and efficiency, aligning with the Mission Control audience and requirements. Its workflows made it possible to go from requirements to high-fidelity ideas quickly, without compromising on clarity.Figma, meanwhile, was indispensable for structure and version control. While Claude drove creative output, Figma provided the rigor required to organize and manage design iterations. The resulting workflow was a two-step process:<ol><li>Generate top-level visual concepts and mockups in Claude. </li><li>Organize, refine, and version those assets in Figma. </li></ol>This “1-2 UXD” approach combined AI’s creative speed with the reliability and professionalism of a best-in-class design platform.<figure id="figure-o1opkv3stpmesogwjy" data-full-width=""><img id="o1opkv3stpmesogwjy" src="https://cdn.durable.co/blocks/12pOuph92UT6xeKZHd5mxQ03m212sFQQTWRuAkBVS524d2lAD4MbKfSJgPDC2sWc.png" alt="claude prompts to figma designs" title="claude prompts to figma designs" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 16/9; object-position: 0% 50%;" data-corners="default" data-aspect="16:9" data-caption="Claude Prompts to Figma Designs" data-positionx="0%" data-positiony="50%" data-fullwidth="true" data-mce-src="https://cdn.durable.co/blocks/12pOuph92UT6xeKZHd5mxQ03m212sFQQTWRuAkBVS524d2lAD4MbKfSJgPDC2sWc.png" data-mce-style="aspect-ratio: 16/9; object-position: 0% 50%;"><figcaption class="body-normal text-gray-500 mt-2">Claude Prompts to Figma Designs</figcaption></figure><h3>From Design to Code: The Implementation Challenge </h3>With finalized designs in Figma, the next challenge was implementation: translating those designs into robust Dart/Flutter code for our front end. This is often a critical pain point, so I evaluated two main approaches.<ul><li>Use an automated exporter to turn Figma designs directly into Dart code. While this promises speed, the downside is often messy, unoptimized code that hampers scalability and maintenance. Work with VS Code Copilot, prompting it with targeted screen grabs of UI components from Figma. By generating code for each widget—cards, buttons, nav bars—individually, the result is far cleaner. </li><li>Building out the UI widget by widget produced a scalable and well-structured codebase, aligning with Flutter best practices. Although more hands-on, this manual, AI-assisted approach set a high standard for implementation quality and long-term maintainability.</li></ul><figure id="figure-dbb5kgntjgmesoknwk" data-full-width=""><img id="dbb5kgntjgmesoknwk" src="https://cdn.durable.co/blocks/29f2XSVImigvWgZTyByDsRn6YbmLgW0vWnTFgyHmMwAOTZfLodYqcD2eUNcK0fhh.png" alt="flutter widget implementation" title="flutter widget implementation" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 16/9; object-position: 50% 50%;" data-corners="default" data-aspect="16:9" data-caption="Flutter Widget-by-Widget Implementation " data-positionx="50%" data-positiony="50%" data-fullwidth="true" data-mce-src="https://cdn.durable.co/blocks/29f2XSVImigvWgZTyByDsRn6YbmLgW0vWnTFgyHmMwAOTZfLodYqcD2eUNcK0fhh.png" data-mce-style="aspect-ratio: 16/9; object-position: 50% 50%;"><figcaption class="body-normal text-gray-500 mt-2">Flutter Widget-by-Widget Implementation</figcaption></figure><h3>The Results: A 2–5x Gain in Design Efficiency </h3>The final workflow took us from product requirements, through interactive mockups, to a production-ready front end. Leveraging LLMs as a UX design partner, I saw a 2–5x improvement in efficiency compared to tackling the process without AI support.This system enabled me to deliver a user-centric design that fully supported the project’s objectives, despite not having formal UX training. Here, AI served as a true force multiplier, transforming product vision into validated design at speed.<h3>Process Improvements for Next Time </h3>Is this approach a “silver bullet” for UX design? Not quite. Mission Control was a time-boxed effort, so I was limited in how far I could refine process and tooling. That said, I see clear paths for optimization in future engagements:<ul><li>Explore more advanced automation and Figma-Futter integrations (e.g., FlutterFlow, Rocket, FlutterViz) to streamline the coding of individual frontend components. </li><li>Bring experienced UX design professionals into the AI-augmented workflow. My productivity gains were limited by my own gaps. With expert oversight, I’d expect results closer to 5–20x improvements—truly transformative. </li><li>Strengthen our Figma asset management with versioning tools and tighter integration into source control, enhancing team workflows and design traceability. </li></ul><h3>Conclusion: Expertise Defines the Exponential Gain </h3>This experience reinforces what we see again and again: expertise shapes the ROI of LLM-powered product development. When applied in areas of deep knowledge—like product strategy or backend architecture—AI can enable exponential acceleration.In UX design, where my experience is lighter, gains were significant but not game-changing. Relying more on the LLM meant more iterations and adjustments. While this isn’t “the blind leading the blind,” it underscores that AI is at its best when paired with capable human leadership. The more you bring to the table, the more powerful your results with AI will be. Stay tuned: Post 5 spotlights the AI-accelerated approach we used for architectural design that helped shape our technology stack.

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