How I Built the AI Chatbot on My Portfolio

When I was redesigning my portfolio, I kept coming back to one question:

**How do I help someone understand seven years of work in five minutes?**

Hiring managers are busy. Recruiters are even busier. Even with well-crafted case studies, most people don't have time to read every project before an interview. I wanted to create something that made my portfolio feel more like a conversation than a presentation.

So I built an AI chatbot.

Not because AI is trendy, but because it solved a real user problem.

Starting with the user
As product designers, we're taught to start with the problem instead of the solution.

The problem wasn't that my portfolio lacked information. It was the opposite. There was too much information spread across multiple case studies, project pages, and experience sections.

What if someone wanted to know:

* What kind of leader am I?
* Have I worked on marketplaces outside of ticketing?
* What's my favorite project?
* How do I approach experimentation?
* Why am I looking for my next role?

Instead of forcing visitors to hunt for those answers, I wanted them to simply ask.

Designing an AI experience
Building the chatbot felt surprisingly similar to designing any other product.

I thought about the questions recruiters ask most often during interviews and organized the information architecture around those topics. I defined the chatbot's tone so it would sound like me: approachable, thoughtful, and confident without feeling scripted.

The biggest challenge wasn't writing code. It was designing the conversation.

Prompt engineering became an exercise in UX writing. Every iteration was about improving clarity, reducing ambiguity, and making sure the AI represented my experience accurately.

Learning the technical side
While I'm a product designer, I wanted to understand enough of the implementation to build something real.

The chatbot uses Claude through Anthropic's API. I built the frontend integration with HTML and JavaScript, then created a secure backend using Cloudflare Workers so my API key never lives in the browser.

Here's the flow:

1. A visitor asks a question.
2. My website sends the request to a Cloudflare Worker.
3. The Worker securely authenticates with the Claude API.
4. Claude generates a response using the prompt I've designed.
5. The answer is returned to the portfolio in real time.

I also learned about API security, CORS, environment secrets, deployment, and how frontend and backend services communicate. It wasn't about becoming a software engineer. It was about understanding enough of the stack to ship a secure product.

AI as a design partner
I didn't write every line of code from memory.

Instead, I treated AI like a senior engineering partner. I used it to help generate code, troubleshoot bugs, explain unfamiliar concepts, and speed up development.

That experience reinforced something I believe will become increasingly important for designers: knowing how to collaborate with AI.

The value isn't memorizing syntax. It's asking better questions, validating solutions, understanding tradeoffs, and knowing when to iterate.

Testing and improving
Once the chatbot was working, I tested it the same way I would any product.

I asked dozens of questions from the perspective of recruiters, hiring managers, and fellow designers. Whenever an answer felt vague, repetitive, or inaccurate, I refined the prompt, reorganized the information, or adjusted how the AI prioritized context.

Each iteration made the experience feel more natural and more representative of how I actually communicate.

What I learned
This project reminded me that great products don't start with technology. They start with people.

The chatbot isn't the most technically complex thing I've built, but it represents something I'm excited about: using AI to create better user experiences.

As product designers, we don't need to become machine learning engineers to build meaningful AI products. We need to understand the capabilities, the limitations, and how to design experiences that make the technology genuinely useful.

For me, this chatbot is more than a portfolio feature. It's a small example of how I like to work: identify a real problem, learn what's needed to solve it, collaborate across disciplines, and keep iterating until the experience feels right.