Today, we’re thrilled to sit down with Dominic Jainy, a seasoned IT professional whose expertise in artificial intelligence, machine learning, and blockchain has reshaped how technology is applied across industries. With a deep understanding of how AI can enhance developer productivity, Dominic has hands-on experience navigating the promises and pitfalls of integrating AI into coding workflows. In this conversation, we’ll dive into the tangible benefits of AI as a tool for troubleshooting, scaffolding, and learning, while also exploring the challenges of maintaining control, avoiding common traps, and ensuring consistency in complex projects. Let’s uncover how AI is truly moving the needle for developers when used with precision and expertise.
How has AI helped you tackle a tricky error in a project, and can you walk us through a specific moment where it made a difference in your troubleshooting process?
I’ve had several moments where AI turned a frustrating debugging session into a quick win. One instance that stands out was during a project where I encountered a cryptic error in a microservices setup—something about a connection timeout that didn’t align with the logs I was seeing. I copied the error message into an AI tool and asked for a plain-language breakdown of potential causes. Within seconds, it suggested a misconfigured retry policy in the service mesh that might be silently dropping requests. I remember feeling a mix of skepticism and relief—could it be that simple? I dug into the configuration, verified the settings against the docs, and sure enough, that was the culprit. What could’ve taken hours of combing through logs and docs was narrowed down in minutes. I still ran a few test cases to confirm the fix held under load, but that initial nudge from AI saved me a ton of mental bandwidth.
Can you share a time when AI took the tedium out of setting up repetitive project structures or boilerplate code, and how did that impact your workflow?
Absolutely, AI has been a lifesaver for boilerplate tasks. I recall starting a greenfield web app a while back, needing to set up the project structure, basic routing, environment files, and a Dockerfile—all standard stuff, but it eats up time. I used an AI tool to generate the initial scaffolding by describing the tech stack and specific needs, like a Node.js app with a certain testing framework. It spat out a solid starting point in minutes, shaving off what would’ve been at least an hour of manual setup. I could almost feel the weight lift off my shoulders as I skipped straight to the creative coding part. Of course, I reviewed every line—tweaked the Dockerfile for our specific CI pipeline and adjusted some paths—but the heavy lifting was done. That time savings let me focus on the app’s unique logic rather than reinventing the wheel.
When faced with a new framework or tool, how have you leveraged AI to accelerate your learning, and how did you ensure you weren’t just relying on it blindly?
Learning new tools is where AI shines as a sidekick. I remember diving into a lesser-known API for a real-time data streaming project—it was powerful but had sparse community support. I turned to AI to get quick examples of implementation, asking for sample code and explanations of key methods. It gave me a working snippet and broke down the core concepts, which was like having a tutor point me in the right direction. I could feel the fog clearing as I connected the dots faster than I would’ve by sifting through sparse docs alone. But I didn’t stop there—I cross-checked every detail with the official documentation to ensure I understood the behavior and constraints. That balance was key; AI got me up to speed, but reading the source material cemented my confidence and helped me avoid any oversights the model might’ve missed.
AI can sometimes lead us down the wrong path with suggestions that don’t work in reality. Have you ever dealt with a misleading output, and how did you handle it?
Oh, I’ve definitely been burned by AI’s overconfidence. One time, I was working on integrating a third-party SDK, and the AI suggested a method call that looked perfect—clean syntax, logical flow, the works. I implemented it, only to find out during testing that the method didn’t even exist in the SDK version we were using. It was frustrating, like chasing a mirage; I wasted a good chunk of time debugging something that was never real. I caught it by running a quick test suite that failed spectacularly, which was my wake-up call. After that, I made it a habit to verify API calls against the exact version docs before integrating anything AI suggests. I also started flagging these “invented API” risks in team discussions, encouraging everyone to double-check before trusting polished-looking code. That experience taught me to treat AI as a brainstorming tool, not gospel.
Have you noticed AI suggestions causing inconsistencies in naming or patterns across a codebase, and how did you manage to keep things aligned?
Yes, I’ve seen what I call “prompt drift” firsthand. On a larger project with multiple modules, I used AI to generate helper functions across different parts of the codebase. Over time, I noticed naming conventions and error-handling styles started to vary—some modules used camelCase, others snake_case, and exception handling was all over the place. It was like watching a quilt come together with mismatched patches; the inconsistency grated on me and made the code harder to maintain. I caught it during a code review and spent extra time normalizing the patterns, which slowed us down a bit. To prevent this going forward, I created a style guide snippet that I included in every prompt, grounding the AI with our conventions. I also kept changes small and reviewed them frequently with the team to catch drift early. It’s a subtle issue, but it can snowball if you’re not vigilant.
Can you tell us about a time when crafting a very specific prompt led to a noticeably better AI response, and how did that play out in your work?
Specificity in prompts is a game-changer. I remember working on a complex authentication flow where I needed help with a token refresh mechanism. Instead of asking something vague like “help with token refresh,” I wrote a detailed prompt specifying the language, framework, and exact behavior I wanted—something like, “Generate a function in Python using Flask to refresh a JWT token with these retry conditions.” The response was spot-on, delivering a snippet that fit our stack and addressed edge cases I hadn’t even mentioned. I could almost hear the gears click into place as I read it—it felt tailored, not generic. I tested it in a sandbox environment, made minor tweaks for our specific error logging, and rolled it out smoothly. That precision in my request cut down on back-and-forth iterations and gave me something usable right away. It reinforced my belief that the effort you put into a prompt directly impacts the value you get out.
How have you used project-specific documentation or examples to ground AI assistance, and what difference did that make in a challenging scenario?
Grounding AI with context is something I swear by, especially in complex projects. I was once tasked with extending a custom internal SDK with strict non-functional requirements around latency and error handling. I fed the AI relevant interface definitions, sample code, and a snippet of our performance constraints right in the prompt before asking for implementation ideas. The difference was night and day—without that context, I’d get generic solutions, but with it, the suggestions aligned with our actual surface area. I remember breathing easier knowing I wasn’t starting from scratch or wading through irrelevant boilerplate. It still needed human judgment to fine-tune for edge cases, but the AI’s output was immediately more relevant and saved me hours of rework. That grounding turned a tool that could’ve been a shot in the dark into a focused collaborator. It’s a habit I’ve stuck with for any project with unique constraints.
In balancing AI assistance with human oversight, can you share an example of how you’ve ensured developers remain in control during a project, and what challenges came up?
Keeping human judgment at the helm while using AI is non-negotiable for me. On a recent team project building a data pipeline, we used AI to draft initial scripts for data transformation and error handling. My approach was to assign clear roles—AI could suggest code and explain patterns, but developers owned design decisions, verification, and final integration. We faced a challenge early on when some team members leaned too heavily on AI outputs without thorough testing, leading to a subtle bug in retry logic that only surfaced under heavy load. It was a tense moment, realizing we’d almost shipped flawed code because of overtrust. I could feel the urgency as we scrambled to fix it. We tightened our process after that, mandating small diffs, mandatory test coverage, and peer reviews for any AI-generated code. That balance—using AI to accelerate but never to decide—helped us harness its speed while avoiding costly oversights. It’s a constant dance, but one worth mastering to keep quality high.
Looking ahead, what is your forecast for the role of AI in software development over the next few years?
I see AI becoming an even more integral part of the developer toolkit, evolving into a seamless assistant for everything from ideation to debugging. My forecast is that within the next few years, AI will get better at understanding project-specific contexts, thanks to improved training on real-world codebases and tighter integration with IDEs. However, I believe the core challenge will remain—ensuring developers don’t cede critical thinking to the tool. We’ll likely see more emphasis on training programs and guardrails built into workflows to keep human expertise at the forefront. I’m excited, but cautiously so; the potential for productivity gains is massive, but only if we treat AI as a partner, not a replacement. What worries me is the risk of younger developers growing overly reliant on it without mastering fundamentals. I think the industry will need to double down on education to strike that balance.