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<h3>Introduction: Synthesizing the Mission Control Project </h3>This series began with a critical question facing modern product organizations: <blockquote>Can Large Language Models (LLMs) deliver true value to real-world development teams, or are they limited to cool-but-shallow prototyping? </blockquote>To answer this, I launched the Mission Control project—a 200-hour, 10-week solo effort where I took on every key role in the product development lifecycle: Product Manager, Architect, Software Engineer, QA Engineer, and UX Designer. The objective was to build a universal, SaaS-style application from the ground up, leveraging LLMs as the principal tool.Throughout the project, I operated under four production-grade standards designed to accurately reflect real-world conditions:<ul><li>Infrastructure: Build on a foundation that demonstrates strong performance, reliability, and scalability. </li><li>Tech Stack: Use a stack that ensures flexibility and extensibility. </li><li>Capabilities: Implement universal, expected features found in enterprise SaaS products. </li><li>Modern Workflows: Employ real-world development best practices, including source control, containerization, modularization, and robust documentation and testing.</li></ul>As outlined in previous posts, the results were decisive. Two primary insights emerged:First: Productivity gains from LLMs are closely tied to the expertise of the human operator. Second, LLMs operate most effectively as highly capable, motivated interns: they excel at well-defined tasks but require clear direction and expert oversight to consistently produce high-quality work.<h3>What's Next? Four Paths to Increased Value </h3>The AI landscape is evolving at an unprecedented pace. With Mission Control now complete, my focus shifts to translating these validated insights into scalable, real-world impact. The project demonstrated that, under proper oversight, LLMs can drive both operational efficiency and product quality. Building on this, I am pursuing four distinct paths to further harness the tangible value of this technology.1. Building Agentic Systems for High-Value Activities The project demonstrated that LLMs are highly effective at automating specific, repeatable tasks that typically consume valuable developer time. The next step is to develop autonomous agents that encapsulate these activities, deeply integrating them into the development pipeline. These agents are not merely scripts—they are intelligent systems that execute complex functions based on key triggers such as code commits or pull request approvals.I am actively exploring the following agent concepts:<ul><li>Developer / QA Documentation Agent: Triggered by a pull request merge to the main branch, this agent analyzes approved code changes, linked tickets, and comments to automatically generate developer documentation and comprehensive test plans. Metrics for success include decreased time spent on manual documentation and reduced knowledge-loss-related defects. </li><li>Unit Testing Developer Agent: On every new feature commit, this agent reviews the code and scaffolds a complete set of unit and system-level tests. It generates test files, mock data, and test case outlines for human review. Success is measured by increased test coverage and a marked decrease in the engineering effort needed to maintain a robust testing suite. </li><li>Code Health Agent: Continuously monitoring the full codebase, this agent conducts architectural analysis to identify redundancies, circular dependencies, and refactoring opportunities. It produces actionable health reports for engineering, directly supporting long-term maintainability and reducing technical debt. Key metrics include improved code complexity scores and enhanced developer velocity. </li><li>Design System Agent: This agent automates the creation and maintenance of a robust design system, directly translating UX design mocks and frontend component development into a unified, version-controlled component library. This ensures consistent brand application, accelerates development cycles, and significantly reduces redundant engineering efforts across all platforms. </li></ul>2. Exploring Emerging Opportunities with LLMs Several emerging trends now present significant opportunities for further exploration. These areas, while still developing, offer natural extensions to the Mission Control foundation.Key Trend #1: Domain-Specific LLMs and Retrieval-Augmented Generation (RAG)The industry is transitioning from general-purpose LLMs to domain-specific models, which offer superior accuracy and reliability for specialized use cases. Leveraging this trend, we can evolve beyond Mission Control by integrating custom-trained LLMs that provide context-aware insights and delivery.Key Trend #2: Multimodal AI for Design-to-CodeRecent advancements allow multimodal LLMs to process and generate text, images, audio, and video. Are there next-step opportunities beyond Mission Control to integrate advanced visual and audio processing capabilities into its design-to-code solutions?3. Delivering Modular, Low-Risk Integrations to Modern Product Clients Insights from Mission Control move beyond theory—they are actionable now. I'd like to partner with organizations to deploy focused, low-risk LLM integrations that generate measurable results without disrupting existing processes. The priority is modularity—identifying areas where LLMs can deliver rapid, high-impact improvement.Low Hanging Fruit Opportunities:<ul><li>For Product Managers: Deploy agents to synthesize market research, generate competitive analysis summaries, and draft Product Requirements Documents (PRDs). </li><li>For Engineering Teams: Integrate bots to produce developer documentation for new APIs, author boilerplate code, and automate the creation of test cases. </li><li>For QA Engineers: Implement assistants to draft comprehensive test scenarios from user stories and automate test data generation.</li></ul>These would be monitored via clear metrics, versioning, and strict governance frameworks—measuring time savings, reduction in defect leakage, and increased feature throughput to ensure concrete ROI.4. Building a Real-World Product on the Mission Control Foundation The most ambitious path is to fork the Mission Control codebase and build a commercially viable product. This initiative goes beyond solo experimentation to validate these principles in a collaborative, real-world environment. By partnering with domain experts where I lack specialized expertise, we can maximize the value generated by our "LLM interns."This is a powerful opportunity for the right technology partner. Mission Control delivers a proven technological springboard and can significantly accelerate new product timelines. I am actively seeking founders with compelling venture ideas who want to leverage a robust foundation. If you are looking to fast-track your product vision, let’s connect.<h3>Conclusion: A Call for Pragmatic Exploration </h3>The Mission Control project proved invaluable by introducing real-world constraints and standards. By operating within authentic development workflows and upholding strict production standards, I obtained clear, practical insights into where LLMs add value—and where caution is warranted. LLMs are not substitutes for expertise; rather, they are high-impact force multipliers that amplify expert productivity.I strongly encourage product development organizations to start with targeted, manageable pilots. Identify recurring process gaps—such as documentation, test scaffolding, or research synthesis—and focus there first. These use cases offer immediate, measurable gains in efficiency and quality.We have entered an era defined by pragmatic, results-driven AI implementation. I look forward to working with product development leaders who are committed to real outcomes. Let’s discuss how pragmatic experimentation and co-building can unlock next-level value for your organization.

Looking Ahead: From Mission Control to Real-World Impact with LLMs

This series has explored one central question: can Large Language Models (LLMs) deliver measurable productivity value to real-world product development teams? To provide a definitive answer, I undertook Mission Control, a solo, timeboxed 200-hour project. In this experiment, I assumed every critical role—Product Manager, UX Designer, Engineer, and QA—leveraging LLMs at every stage to accelerate progress.The verdict is in: yes, LLMs can provide value to real world teams. However, the magnitude of that value is directly proportional to the expertise of the person guiding them. Throughout this project, LLMs acted as brilliant, tireless interns. They can research, draft, and build at an incredible pace, but they require expert direction to produce high-quality work and avoid costly mistakes. Without strategic oversight, their output can be generic, flawed, or entirely misaligned with project goals.As clarified in a previous post, my level of expertise varies across the different roles I assumed as part of this project. I am an expert when it comes to Product Management. I have intermediate Software Engineering and QA Engineering skills (where I started my career). I have very little experience/skills when it comes to DevOps and UX/Design. The productivity grades that I will highlight in the next section, as you'll see, are directly correlated to my level of experience and expertise that I was able to apply when guiding or managing my LLM "interns."&nbsp;<h3>LLM Productivity Grades by Functional Area </h3>To quantify the impact, I graded the productivity gains observed across each discipline involved in building Mission Control. The results were not uniform, highlighting how LLMs performance truly relies on the level of expertise guiding them.Product Management: A- (Solid 2–5x Gain) LLMs proved to be a powerful ally in the foundational stages of product definition of Mission Control. Tasks like framing the product vision, drafting initial Product Requirements Documents (PRDs), and identifying core value propositions were significantly accelerated. The ability to synthesize market research, summarize competitor analyses, and brainstorm in/around user personas in minutes instead of days was a clear win.&nbsp;However, LLMs struggle without domain-specific context. Left unguided, their suggestions were often over-generalized and lacked strategic nuance. The real breakthrough came from using expert-level prompts that embedded constraints, business goals, and a deep understanding of the target user. I used my 20+ years' experience in Product Management and Leadership roles to prompt the LLM to a deeper level of product strategy.Architectural Design &amp; Tech Stack Selection: A+ (Significant 10–20x Gain) Architectural decisions carry long-term consequences, and LLMs were instrumental in de-risking this process. I utilized them to conduct deep comparisons of backend infrastructure options, database technologies, service layer patterns, and frontend frameworks. Generating comprehensive tables that detailed the pros, cons, and tradeoffs of different choices—complete with implementation considerations—was remarkably efficient.&nbsp;The primary caveat here is the LLM's tendency to "hallucinate" or present outdated information. It occasionally referenced outdated documentation of platforms that have recently gone through major updates. Success required a strict validation workflow: use the LLM to generate options and structure the analysis, but always verify every claim against official documentation and trusted community sources. Human oversight was critical to separate insightful recommendations from plausible-sounding falsehoods.But, at the end of the day, I ended up with a very solid architectural design and the tech stack for Mission Control is one I would use with confidence for any new product venture foundation.UX Design: B (Solid 2–5x Gain, but in narrow focused areas) In UX design, LLMs demonstrated solid utility in translating requirements into tangible structures. I tasked them with generating wireframe concepts and basic mockups based on product requirements, which served as an excellent starting point for iteration. They were also effective at structuring frontend components that could be leveraged across the application, helping maintain consistency. This workflow accelerated the initial stages of UX design and frontend implementation.The limitations became apparent in areas requiring visual nuance and complex information hierarchy. LLMs can arrange elements on a page, but they lack a true designer's eye for balance, flow, and emotional resonance. The most effective workflow involved pairing the LLM's structural speed with human judgment. I used it to quickly scaffold layouts and then applied my own expertise to refine the details, polish the user journey, and ensure the final design was not just functional, but intuitive and engaging.In narrow focused areas around visual mocks creation and frontend component translation, the LLMs provided a solid productivity boost, but I grade this area a 'B' because I wish I could have leveraged LLMs for much more solid UX wireframing and complex flow design.Software Engineering: A+ (Exponential 10–50x Gain) This is where LLMs delivered their most transformative impact. Across both frontend and backend development, the productivity gains were exponential. LLMs excelled at scaffolding new features, writing comprehensive unit and integration tests, refactoring complex code blocks, and generating database migration scripts. The ability to instantly connect to APIs, handle data transformations, and boilerplate entire modules turned hours of tedious work into minutes of prompting and review.There was risk associated with all of this that I experienced and had to learn how to mitigate and/or remediate. LLMs can introduce subtle logic bugs, forget really important context of recent changes, rely on outdated or insecure libraries, and/or produce code that is functional but not optimal. To mitigate this, a rigorous process is non-negotiable. I found success by enforcing a "technical design, phased build / documentation, manual test" cycle:<ul><li>Technical Design: Design a feature with the LLM as a design partner, leveraging product requirements and targeted prompts to not only come up with a good technical approach to building the solution, but an understanding of the tradeoffs or risks as well. This technical design would help me understand exactly and sign off on the approach, and provide an up front opportunity to break up this "mini project" into phases or chunks. </li><li>Phased Build &amp; Documentation Iterations: I would move into an iterative motion with the LLM to build smaller chunks of the "mini project." I also got into the good habit of having developer and testing documentation created along the way.&nbsp; </li><li>Manual Testing: Leveraging the big picture technical design and the iterative developer and test case documentation, I would then do a manual testing / verification pass on that phase of work before moving the LLM into the next. </li></ul>For the most part, software engineering on the backend and frontend with LLM's was incredibly productive. I consider myself to have intermediate developer skills, but I think it was just enough to help me "keep the LLMs honest" as we developed Mission Control.&nbsp;(The one exception to this trend was: DevOps (Grade: C, Modest 1.5–2x Gain). While the LLM could generate configuration files and suggest auth and user / system permissions changes, the ecosystem's complexity, frequent version mismatches, and subtle dependency conflicts demanded heavy manual iteration and verification. I encountered a lot of frustrating, mystifying blocks along my DevOps journey. To be fair, it should be noted that I have very little expertise when it comes to DevOps, so in effect I was a poor "LLM Intern" manager in this area.)QA Engineering: A+ (Exponential 10–50x Gain) For quality assurance, LLMs were a force multiplier. I leveraged them to generate exhaustive test plans from product requirements, discover obscure edge cases, and create detailed checklists for exploratory testing sessions. They were also highly effective at writing SQL queries for data validation and parsing dense server logs to quickly identify the root cause of an error. This freed up significant time to focus on higher-level testing strategy and actual bug-fixing.The key guardrail was ensuring traceability and avoiding false positives. Prompts needed to be precise to ensure the generated test cases directly mapped back to specific user stories or acceptance criteria. For log analysis, the LLM’s findings always required cross-validation with the source data to confirm its interpretations were accurate.This is another functional area for which I have experience and expertise from a "past professional life" - I was able to apply that expertise in getting the most of of the LLMs during the Mission Control project.<h3>Putting It All Together: LLM Interns Are Worth It </h3>Across the entire product development lifecycle, the net result was overwhelmingly positive. The crucial caveat remains: LLMs perform best when treated as high-leverage interns. They need clear instructions, contextual awareness, and expert oversight to stay on track and produce work that meets professional standards.The overall impact on the Mission Control project was profound. A solo 200-hour timebox produced a production-quality application built on a modern, strategic tech stack. By my estimation, this same project would have required close to 2,000 hours of effort if tackled alone without LLM assistance. The models compressed the timeline by an order of magnitude.For teams evaluating the ROI of integrating LLMs, consider this framework:<ul><li>Candidate Tasks: Identify repetitive, template-driven, or research-intensive work. </li><li>Risk Profile: Assess the cost of an error for each task. Low-risk tasks (e.g., drafting internal docs) are great starting points. </li><li>Oversight Model: Define who reviews the AI's output and what the review criteria are. </li><li>Measurement: Track metrics like cycle time, code review throughput, and defect escape rates to quantify impact. </li><li>Change Management: Prepare your team with training and clear guidelines for effective and responsible use. </li></ul><h3>Conclusion </h3>My recommendation for product leaders, founders, and engineering teams is to adopt LLMs through a phased "crawl, walk, run" approach. The potential for productivity gains is too significant to ignore. There are numerous low-hanging-fruit opportunities uncovered in this project that can deliver immediate value with minimal risk.Start with tasks like synthesizing user feedback, drafting initial PRDs, generating comparison tables of new technology candidates, scaffolding boilerplate code, writing unit tests, and creating data validation queries. By integrating LLMs in a focused and measured way, you can begin unlocking their power while building the internal expertise needed for more advanced applications.In the final post of this series, we will look ahead. I will discuss the next steps for the Mission Control project, outline key areas for deeper exploration in AI-assisted development, and share my plans for continuing these experiments with emerging LLM technologies.&nbsp;

LLM Productivity Verdict: Grading AI's Real-World Impact

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.

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