How I Built an AI-Powered Portfolio Assistant with Google Gemini

How I Built an AI-Powered Portfolio Assistant with Google Gemini

TL;DR: I built an AI assistant into my portfolio at hailamdev.space that can answer questions about my skills, projects, and experience — powered by Google Gemini 2.0. Here's exactly how I did it.

The Problem

As a Fullstack Developer based in Ho Chi Minh City, Vietnam, I wanted my portfolio to stand out. Traditional portfolios are passive — visitors scroll, look at screenshots, and leave. I wanted something interactive.

What if recruiters and potential clients could ask questions about my experience and get instant, intelligent answers?

The Architecture

User Question → Intent Detection (local) → Gemini API → Structured Response → Rich UI Card

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Key Components

• Intent Detection Layer — Detects common patterns locally before hitting the API

• System Prompt Engineering — Provides Gemini with full context about my profile, projects, and skills

• Structured Output — Gemini returns JSON with answer, highlights, quickFacts, skillsMatrix

• Rich Response Cards — React components render structured data beautifully

Tech Stack

Component Technology

Frontend React + CSS (Brutalist theme)

AI Model Google Gemini 2.0 Flash

Hosting Vercel

Animations GSAP + Three.js

Analytics Google Analytics 4

Intent Detection — Saving API Calls

Before calling Gemini, I detect common intents locally:

if (/cv|resume|download/.test(lower)) return 'cv'; if (/contact|email|hire/.test(lower)) return 'contact'; if (/project|portfolio|work/.test(lower)) return 'projects'; if (/skill|technology|stack/.test(lower)) return 'skills';

return null; // Falls through to Gemini };`

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This handles ~40% of questions without API calls, reducing latency and costs.

Structured Prompt Engineering

The secret sauce is the system prompt. Instead of letting Gemini generate free-form text, I instruct it to return structured JSON:

const systemPrompt = You are an AI assistant for Nguyễn Xuân Hải's portfolio. Always respond in JSON format: { "answer": "Natural language answer", "highlights": ["Key point 1", "Key point 2"], "quickFacts": [{"label": "Experience", "value": "2+ years"}], "skillsMatrix": [{"skill": "React", "level": "advanced", "evidence": "5+ projects"}] } ;

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This gives me full control over the UI while letting Gemini handle the intelligence.

Features I'm Proud Of

1. Multi-Language Support

The assistant handles both Vietnamese and English seamlessly, detecting language from user input.

2. Job Description Analysis

HR recruiters can upload a JD and the assistant analyzes fit against my profile — showing matching skills, gaps, and recommendations.

3. Slash Commands

Power users can use commands like /cv, /pdf, /clear for quick actions.

4. Response Styles

Users can switch between brief, detailed, and fit modes for different use cases.

Results

Since deployment:

• Average session duration increased significantly

• Recruiters spend more time interacting vs. just scrolling

• The assistant handles questions I'd normally answer via email

Try It Yourself

Chat with my AI Assistant — ask it anything about my skills, projects, or availability.

Full Portfolio — explore my projects, experience, and 3D animations.

About the Author

Nguyễn Xuân Hải is a Fullstack Developer based in Ho Chi Minh City, Vietnam, specializing in React, .NET Core, Node.js, and AI integration. His commercial projects include Great Link Mai House (B2B real estate platform), English Community Hub (educational platform), and VN Media Hub (CMS platform).

📫 Contact: [email protected] | GitHub | Portfolio