The rise of AI-assisted coding tools is reshaping how software is developed. From speeding up prototyping to enabling non-developers to create applications, the impact is profound.
In this blog, we explore the transformative impact of Vibe and AI-assisted coding on software development, based on an insightful conversation between host Amit Chaudhary (CEO & Founder at Enqurious) and Supreet Tare (Founder at Respondr.ai). The discussion covers how AI is revolutionizing the way developers build applications, practical challenges faced when working with AI-generated code, and best practices for leveraging AI tools effectively. Whether you’re a developer, manager, or tech enthusiast, this blog offers a clear overview of how AI-driven coding is reshaping the software landscape and what it means for the future of development.
I’ve spent over 17 years in backend development, working on large enterprise-grade projects. But a while ago, I became curious about how generative AI could improve our development process. I started experimenting with tools, and while I was skeptical at first, I quickly saw how they could accelerate prototyping and remove a lot of repetitive work. That’s when I realized this isn’t a shortcut or a magic trick — AI is like a very capable assistant. It takes time, effort, and the right mindset to really make it work.
The biggest shift I’ve noticed is how fast you can build an MVP now. What used to take weeks or even months can now be done in days. These tools are great at handling boilerplate code and even front-end design — something I’ve traditionally not been strong at. But it’s important to remember: this isn’t hands-off development. You still have to iterate, prompt, test, and refine. AI doesn’t replace developers — it enhances them.
I always treat AI like a junior developer — smart, but not always context-aware. You have to set guardrails. I make it a habit to include a line in my prompts like: “Do not change 95% of the code unless you’re 100% sure, and ask questions if unsure.” That alone saves me from a lot of trouble. I also check everything into version control frequently. These small practices go a long way in keeping things stable.
We’re in a very interesting phase right now. Everyone’s learning — some of our HR and ops folks have started building their own apps! So, instead of a fixed team structure, people are working more independently, experimenting and learning through their own mini-projects. But we still need to share what we learn, which is why we’ve created a Slack channel where everyone can post prompts, results, and lessons. It’s very informal, but it works.
It all starts with the business problem. Once I know what I want to build, I ask the AI to just create the prototype — not the full app. That keeps things manageable. I write very specific prompts, ask it to stick to one screen or feature, and build from there. I don’t even bother giving it Figma files or design references; I let it take a first shot. Often, the result surprises me in a good way. From there, I tweak colors, layout, animations — all in small steps.
Sure. I recently built a prototype for my agency’s marketing website focused on voice and conversational AI. I prompted the AI to generate a modern, responsive landing page showcasing three specific services. I kept the scope small — one page only — and iterated. In under a week, I had an MVP that looked good and was production-ready. Compare that to traditional development, and you’re saving weeks of effort.
We use a dedicated Slack channel where everyone shares their best prompts, feedback, tips — anything that helps others get better outcomes. Right now, it’s unstructured, but it’s already proving super helpful. Just seeing how someone else tweaks a prompt or adds examples can be a big eye-opener. Our plan is to formalize this later, but the informal sharing is a great start.
Replit is hands down my favorite. As someone who’s always been a backend developer, I struggled with front-end work. But Replit makes me feel like a full-stack dev. I can generate UI, logic, and even deploy — all in one place. It gives me control and confidence, which is empowering. I used to need a team to build things end-to-end — now I can do it solo if needed.
That’s a common issue. I always add that one line I mentioned earlier — the 95% rule. Also, I make sure to be as specific as possible in feedback. If a section isn’t working, I’ll refer to it directly, include images if needed, and explain my expectations clearly. The AI adapts better when it knows exactly what you want — and what you don’t.
Yes — and it’s a mixed bag. I often use Claude, GPT, DeepSeek, Manus, and others to help draft structured prompts. But funny enough, when I take those prompts into Replit, they sometimes don’t work well. It’s like each AI has its own personality. You have to understand what each tool is good at, and build your workflow around that.
Patience. That’s the number one thing. Don’t expect to prompt once and get a finished app. Think of AI as a smart intern — they can do a lot, but only with clear guidance and feedback. Start with small projects, iterate, and be okay with reworking things. The more you engage with the tools, the better your outcomes will be.
AI coding tools significantly reduce development time but require a clear, iterative approach—think of AI as a smart assistant needing guidance rather than a magic wand.
Clear communication and version control are crucial to avoid unwanted changes, ensuring AI only modifies what is intended.
Collaborative knowledge sharing, especially prompt engineering best practices, accelerates team learning and improves AI-assisted coding outcomes.
AI-assisted coding is transforming software development by making it more accessible, faster, and iterative. It shifts the traditional team structure and requires developers to adopt new workflows and mindsets. Whether you’re a backend developer, a non-technical team member, or a startup founder, learning how to effectively harness AI coding tools can be a game changer in your projects.
Want to hear the full story? Watch the full episode here to dive deeper into how AI-native coding is reshaping software development with Supreet Tare and Amit Chaudhary.
Explore the AI-native mindset with Nitin Gupta, Founder of FlytBase, as he discusses how businesses must embrace AI from the core to stay relevant in the evolving tech landscape. In this podcast, learn how AI enhances team productivity, and sparks creativity.
This conversation helps understand the meaning of embeddings via lucid examples in cricket
A conversation between a mentee and a mentor to clarify why Snowflake should be a preferred choice for enterprise customers
A conversation between a mentee and a mentor to clarify that datawarehouses are different from databases and are purposely designed for analytical purposes