The Real Picture of Modern Product Engineering in the Age of Generative AI
- ANAND BHUSHAN
- Aug 7
- 4 min read
– A Hands-on Perspective
In a world increasingly shaped by artificial intelligence, product engineering is experiencing a powerful transformation. We’re hearing bold claims:
“Build your app with just a prompt!” “No need for developers — AI will code everything!”
But how true are these claims?
Is Generative AI really capable of replacing the traditional way we build digital products?
As someone who has personally explored the full spectrum — from GenAI-based no-code tools to manual code refinement in Visual Studio Code — I want to share the truth. It’s a mix of promise, power, and present limitations.
Let’s uncover the real picture — not the fantasy, but the facts from the frontlines.
Where We Stand Today
Product engineering today is no longer confined to traditional methods. Developers and creators now use tools like GitHub Copilot, ChatGPT, Replit, and Bolt New AI to scaffold apps, generate code, design UI, and even write test cases. These GenAI tools can:
Understand natural language requirements
Translate them into code
Generate UI screens
Write documentation
Create pipelines
Suggest architecture
But can they build and deploy a full-fledged, secure, scalable product end-to-end — without human intervention?
Let me share my experience.
My Experiment: Building a Real Mobile App Using GenAI
I started with a bold goal — to let AI build an entire mobile application for me using Bolt New AI’s paid platform, believing it could handle everything from UI to backend logic.
Here’s what actually happened:
What Worked Amazingly Well
React Native App Built with Just Prompts
I successfully developed a cross-platform mobile app (iOS + Android) using only high-quality prompts.
UI/UX Screens Were Professional-Grade
The platform generated stunning, modern UI screens — login pages, dashboards, settings, all ready-to-use.
End-to-End Prompt-Based Workflow
Even bug fixes, refactoring, and feature changes were done by simply tweaking prompts. The system responded intelligently.
Token-Aware Development Discipline
Since I was on a paid version, I had to plan my prompts carefully. This taught me a new kind of discipline — prompt design as a resource-efficient skill.
Agent-Based Backend Code with MCP Architecture
I even structured the backend using a Multi-Agent Control Plane (MCP) pattern. The AI created agents for different tasks (data, auth, notifications) and scaffolded their interactions effectively.
Solid MVP Developed
I was able to deliver a working pilot version of the app — functional, integrated UI, modular backend, and navigational flow.
Where It Hit the Wall
Integration with APIs & External Apps Became Difficult: Once I reached the point of integrating with real external systems (APIs, auth services, databases), the AI’s capabilities began to break down. Context was limited, and logical assumptions didn’t always match real-world implementations.
Export & Continue in VS Code: I had to export the codebase and shift to Visual Studio Code, where I continued development using GitHub Copilot and ChatGPT as assistants.
Code Quality ≠ Production-Ready: The generated code, while functional, needed:
Optimization
Cleanup
Security hardening
Performance improvements
GenAI still struggles with non-functional requirements.
Debugging Was a Prompt-Marathon: When errors occurred, it often took multiple prompt iterations to fix them. Explaining errors to the AI, waiting for contextual understanding, and getting valid responses was sometimes slower than fixing them manually.
Over-Generation of Code: The codebase was bloated in some places. It got the job done, but left a trail of inefficiencies that required human cleanup.
Key Insights & Realities
GenAI is an Accelerator — Not a Replacer
It can supercharge your development speed, but critical thinking, architecture decisions, and system design still need humans.
Prompt Engineering Is the New Skillset
Writing high-quality prompts is now as important as writing clean code. It affects cost, output quality, and debugging speed.
Enterprise-Grade Product Engineering Still Needs Traditional Guardrails
Things like:
Security
Compliance
Performance tuning
Scalable data pipelines
…require engineering expertise, not just prompts.
Legacy Systems? Even Harder.
GenAI agents are not yet mature enough to autonomously handle the messy realities of legacy modernization. They can assist with documentation, refactoring, or code summarization — but not lead the transformation.
The Future: Where We’re Heading
The future is bright — but we must be realistic. Here’s how I see the evolution:
GenAI is evolving rapidly, but true end-to-end autonomous product engineering is still a few chapters away. Until then, the human-AI hybrid model remains the most effective.
Yes — AI can help you build apps from scratch. It can design, code, and test better than many junior devs.
But no — it cannot yet deliver complete production-grade products on its own.
Best way forward? Let AI do the heavy lifting, but let humans make the final call. This is not a replacement. It’s evolution — and we’re in the early chapters.
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