Product development in the AI era

In today's AI-powered world, coding has become significantly more accessible. Rather than memorizing complex syntax, we can now communicate with AI using natural language, letting it handle the technical implementation. This advantage has enabled me to develop multiple products that would have seemed impossible before the AI revolution.

Yet these development experiences have revealed that product development extends far beyond writing code. Ironically, even with AI assistance, understanding coding and technical knowledge has become more important for successfully launching products. AI frees me from the mechanics of writing code, allowing me to focus on broader aspects like ideation, architecture, and platform selection.

My recent experience developing a personalized vocabulary bot perfectly illustrates this journey. Though the concept seems straightforward, the development process taught me what comprehensive product development truly requires. Here are the crucial steps I followed:

1. Product idea generation: Before diving into coding, I needed to clearly define my objectives. This meant asking: What functions should the product perform? How would I interact with it? What key qualities must it possess? After multiple iterations, I refined the concept to an "always-on" vocabulary bot that could both add new words to my personal dictionary and quiz me with random selections whenever requested.
2. Full-stack design: Transforming an idea into something tangible requires thorough technical planning from back-end to front-end. Though users might see just a chatbot interface, the system needed multiple integrated modules. With AI consultation, I identified four critical components: a front-end for user interaction, a back-end to process commands and orchestrate different modules, a database to store and retrieve vocabulary, and an AI API to provide contextual examples for newly added words.
3. Platform selection: This stage revealed the complexity of real-world systems. I needed to evaluate platforms based on functionality, cost, computing power, user experience, and available services. Prioritizing functionality, simplicity, and affordability, my AI assistant and I reviewed over 20 options before selecting our solution: Telegram for the front-end (free, cross-device syncing), Vercel for back-end deployment (free "always-on" cloud hosting), Notion as the vocabulary database (familiar interface with free student package), and OpenAI API for word context generation (approximately $4 per 1,000 words—the only cost).
4. MVP development: The actual coding began only after establishing concrete specifications for functionality, architecture, and platforms. With these clear parameters, AI could fully apply its programming capabilities. This proved surprisingly straightforward—I simply provided all my requirements and watched AI generate the first draft. The code passed initial testing on the first attempt, allowing us to proceed directly to the next phase. This success demonstrated how crucial well-defined prompts are for AI-assisted development.
5. Deployment and debugging: Making the program production-ready for cloud-based, "always-on" operation was the most challenging and critical step. Deploying code to Vercel and connecting all platforms through APIs and integrations presented numerous obstacles. Vercel imposed specific requirements, generating complex error messages that required careful analysis. After 16 rounds of iteration with AI—sharing each error and implementing fixes—we finally achieved an error-free state. The moment the product became production-ready was indescribably satisfying.

This development process profoundly deepened my understanding of product development in three key ways:

First, I developed systems thinking. I now evaluate products holistically, considering not just front-end interactions but the entire stack—the underlying modules, back-end operations, and platform integrations that enable each function.

Second, I cultivated an engineering mindset. Previously, I described product requirements through documents and slides from a visual perspective. Now, I also consider technical specifications, associated costs, API requirements, and deployment environments.

Third, I adopted a proper developer workflow. I can now work effectively through distinct development phases—PRD, MVP testing, and deployment—understanding what each stage requires and how they collectively bring a product to life.

Most importantly, I've become more eager to learn. I recognize that product development encompasses far more than coding. While AI remains an invaluable assistant, personally mastering these technical concepts is more important than ever.

Let's learn to build!