Claude Workshop : Developing an AI Smart Interviewer from Scratch
Prakash Software Solutions conducted a live workshop to showcase how AI can turn ideas into working software faster than ever.
Imagine having an app idea today—and seeing it come to life within the same hour.
No code to write, no complicated setup to wrestle with—just sharp ideas, the right prompts, and AI handling the build process.
That’s exactly what this workshop explored. In this session, we built AI Smart Interviewer, a proof of concept (POC), to demonstrate how modern AI tools can create software using prompts instead of traditional coding.
Understanding the requirements came first—and it shaped everything that followed.
Getting Started
Developing software has become significantly simpler with the introduction and inclusion of AI and related technologies. It’s worth noting, however, that AI’s capabilities extend far beyond assisting with code or simply speeding up development.
In this Claude workshop session, we explored something more practical—can AI act like a real development partner, much like an AI development company would for businesses building new products?
To test this, we took on a challenge: Build an AI smart interviewer in just 60 minutes using Claude Code. No hand-written code, no traditional development cycle—just well-crafted prompts guiding the entire build.
Aftermath? A working application that showed how clarity, context, and iteration can dramatically speed up product development.
Requirements First, Everything Else Follows
It’s easy to assume AI can replace planning. It can’t.
Before generating anything, the team defined what the product should actually do.
The goal was to build an AI-powered interview platform that makes hiring a breeze for recruiters—from setting up interviews to reviewing how candidates stack up. It lets you create and schedule interviews, set time limits and customize settings, and share links with candidates super easily.
Plus, AI generates spot-on interview questions, automatically checks responses, and lays out the results in a clean dashboard so you can review and decide fast.
AI is powerful, but it still needs clear direction to work well. Without proper requirements, it doesn’t build exactly what you need—it makes assumptions and guesses. This is also why businesses increasingly rely on AI consulting services before beginning product development.
Kicking Things Off with Claude
Once the requirements were clear, the next step was to give Claude the full context. The team uploaded the document and asked Claude Code to generate the project structure along with a CLAUDE.md file.
This file served as the project’s blueprint, outlining everything needed to guide the build from the start. It defined how the project would be structured, the technologies powering it, and the environment needed to run it smoothly. It also laid things out clearly for API integrations, included database references for handling data, and established workflow rules to keep the development process organized and consistent.
Instead of jumping into features, Claude first built the foundation. All things considered, this decision helped in keeping everything aligned in the long course of our project.
From Idea to Product
This is where imagination turns into reality. Instead of building everything at once, the team worked step by step.
This is key—because AI performs better with structured execution.
Phase 1: Creating the Project Structure
The first prompt generated the entire application skeleton:
- Backend server
- Recruiter dashboard
- Candidate web app
- Mobile structure
- Core configs
What usually takes hours was done in minutes. And this wasn’t just theory—it resulted in a visible interface.
This shows how the application looks from a recruiter’s perspective.
Phase 2: Building the Database
Next came the data layer.
Claude generated a PostgreSQL schema to handle the core parts of the platform, from managing interview schedules and validating candidates to storing questions, responses, scores, and final results.
It also built all essential database optimizations such as indexing and constraints, ensuring that the system was structured for both reliability and performance from the start.
So, this wasn’t just data—it was a working system behind the scenes.
Phase 3: Developing the Backend Logic
With the database ready, the backend came next.
Claude generated APIs using Node.js and Express to power the platform’s key workflows, including candidate verification, interview session management, AI-driven question generation, automated response evaluation, result storage, and notifications. Each API was designed to connect the different parts of the system seamlessly and keep the interview process running smoothly.
One standout feature?
Instead of fixed questions, the system generated them dynamically. Making the interview feel more adaptive instead of redundant.
Phase 4: Designing the Candidate Experience
Now let’s talk about the user journey—because this is where things feel real.
Interview Access Page
Candidates enter their email to access the interview.
Rules and Readiness Screen
Before starting, candidates see clear instructions and expectations. This ensures they are prepared before the interview begins.
Live Interview Interface
This is where the actual interaction happens. The interface includes:
- Real-time AI conversation
- Dynamic questions
- Timer
- Progress tracking
- Structured responses
It closely replicates a real interview environment.
Phase 5: Building the Recruiter Dashboard
Everything on the recruiter side is managed through a centralized dashboard designed to keep the entire interview process simple and organized. From one place, recruiters can create interviews, define interview rules, track candidate activity, monitor progress in real time, and review results as they come in. This is what transforms the platform from just a proof of concept into something genuinely practical and usable.
Managing Interviews with Ease
Beyond setup and monitoring, recruiters also have the flexibility to manage interviews as needed. They can make updates to existing interviews, send notifications to candidates, and revisit detailed results whenever required, giving them full control over the hiring workflow.
This shows how interviews are managed after creation.
The Turning Point: Better Prompts = Better Results
While testing, we came across a unique obstacle: right after the interview, the system disclosed the scores to the candidates; this wasn’t really intentional.
Instead of rewriting the code from scratch, the team refined the prompt. They clearly explained the issue, defined the expected behavior, and added the necessary constraints to guide the fix. With that added clarity, Claude was able to update the implementation and resolve it.
What Truly Came Out of This Workshop
This session showed that working with AI is really about working smarter. Clear thinking still matters, good structure makes things easier, and improving as you go is part of the process. It also showed why many firms now turn to an AI development company for faster execution and better product outcomes. AI doesn’t replace what you can do — it helps you move faster and brings out better results when used the right way.
What You Can Take Away from This
If you’ve been waiting to build something, take this as your sign to start. Start small, be clear about your idea, and give it the right direction. Build step by step, improve as you go, and don’t stress about knowing every technical detail. What matters most is having a clear vision of what you want to create and being able to express it well, which is often where AI consulting services add the most value.
Bottom Line
This workshop proves that building software is no longer limited to developers alone.
With the right approach—clear requirements, structured prompts, and continuous refinement—you can turn ideas into working products faster than ever before, whether independently or with an AI development company.
The real shift isn’t just about speed, it’s about confidence. Once you see an idea come to life this quickly, it changes how you think about building. You stop waiting for the “right time” or perfect conditions and start creating.
