This blog summary traces my journey from using AI as a simple code generator to leveraging it as a strategic architect and product owner. By refining my prompting strategies and treating the AI as an expert consultant, I unlocked higher-quality outputs, critical analysis, and automated quality assurance.
1. The Consultant Phase: Learning to Frame Context
My journey began with functional, context-heavy requests. I realized early on that providing the AI with the "big picture" was crucial for productivity.
The Contextual Foundation: Instead of asking for snippets, I started by asking the AI to
"Analyze the codebase to generate guiding instructions for AI agents.". This ensured the AI understood the architecture, workflows, and conventions before writing a single line of code.Formatting vs. Architecting: Initially, I used the AI for lower-level tasks. However, I quickly learned that asking for readability wasn't enough; I needed to invoke expertise.
2. The "Expert Persona" Shift: Demanding Standards
The pivotal moment in my evolution occurred when I stopped asking for code and started asking for standards. I shifted from telling the AI what to do to telling it who to be.
Invoking Domain Authority: When I prompted,
"As an entity modelling expert, improve the current enitty modelling, also adhere to best practices and standards", the output quality skyrocketed. The AI didn't just format text; it introduced 3NF normalization, temporal awareness (audit fields), and enforced integer cents for money handling—best practices I hadn't explicitly requested.Deep Analysis: I pushed this further by asking,
"Identify all derivable fields from the data model.". The AI successfully identified over 71 derivable fields, allowing us to reduce the database schema by 37% by calculating values rather than storing them.
3. The UI/UX Strategist: Elevating Design & Usability
I realized the AI could do more than backend logic; I challenged it to act as a senior designer to audit and unify the user experience.
Systemic Auditing: I asked the AI to
"Act as a UI/UX expert: audit the card components and propose a unified design system.". Instead of small CSS fixes, the AI performed a consistency audit, identifying disjointed styles and proposing a "Unified Card Architecture" with semantic variants (Form, Data, Stat, Highlight) to reduce cognitive load.Solving UX Pain Points: When I expressed frustration with standard inputs by stating,
"Replace the default number inputs to remove the spinner arrows,"the AI didn't just remove the arrows. It analyzed the accessibility and mobile experience issues, recommending and implementing a custom MoneyInput component usinginputMode="decimal"for a cleaner, professional user experience.
4. The Architect Phase: Delegation and Consolidation
As trust was established, I moved to high-level delegation. I began assigning meta-tasks that required the AI to understand the relationship between files and project structure.
Tech Stack Selection: I leveraged the AI as a CTO, asking,
"Recommend the best frontend and backend tech stack for this implementation.". The AI provided a reasoned matrix for using NestJS and React with TypeScript, rationalizing the choice based on type safety and financial data integrity.Information Architecture: I delegated the organization of documentation entirely:
"Rename documents using a better convention and consolidate them into the minimum number of files.". The AI consolidated 6 redundant files into 4 essential documents, removing over 2,000 lines of duplicate content while improving discoverability.
5. The Quality Assurance Lead: Enforcing Consistency
I evolved to use the AI as a Lead Developer to enforce patterns and catch discrepancies that a human eye might miss.
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Pattern Recognition: I issued a directive to
"Review the code and make sure all similar classes are implemented in same way". The AI successfully identified inconsistencies in DTO naming and method signatures across 8 different services, standardizing them to a single pattern.
6. The Adversarial Partner: The BA/PO Persona
The final and most advanced stage of my evolution was engaging the AI as a Product Owner (PO) to challenge my own assumptions.
Socratic Dialogue: When deciding on date validations for bills, I asked the AI to
"Think like a Product Owner: argue against my decision if you have a different opinion.".Refining Logic: This prompt led to a critical realization about the difference between "transaction dates" (historical, cannot be future) and "bill due dates" (planning, can be future OR past for bookkeeping). The AI successfully argued against my initial constraints, leading to a more flexible and realistic product design.
Conclusion:
By moving from imperative commands ("write this code") to role-based delegation ("act as an expert," "argue with me"), I transformed the AI from a tool into a collaborative partner. This shift allowed me to build a production-ready application with high test coverage, consistent architecture, and solid business logic in a fraction of the standard development time.
Editor's Note: This blog post was written by an AI. It was generated using the following prompt:
"Here is the GitHub Copilot chat summary. Please create a blog summary focused on my evolution as an agentic AI developer—specifically how I learned to work with AI, improved my prompting strategies, and achieved my goals more effectively over time. You should be able to differentiate between my prompts and the AI's replies."
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