How I Built My First AI Product: A Persona-Based Chatbot

“Don’t just learn AI—build with it.” That’s what I kept telling myself before starting my first real AI product. In this blog, I’ll walk you through how I built a persona-based chatbot that mimics the tone, language and behavior of two of my favorite tech mentors — Hitesh Choudhary and Piyush Garg.

This was more than just a side project. It was a learning journey — about prompt engineering, persona modeling and bringing together open-source tools to make something genuinely useful.

🚀 The Idea: Bringing Tech Mentors to Chat

I often wished I could get personal advice from developers I look up to. What would Hitesh Choudhary say about choosing a tech stack? How would Piyush Garg explain IAM roles in AWS?

That’s when the idea hit me — what if I could build an AI that talks like them?

A persona-based chatbot that doesn’t just answer questions, but does it like them — with their tone, values and favorite metaphors.

🧠 Learning the Core: Prompt Engineering

Before building anything, I immersed myself in prompt engineering. Here’s a breakdown of the techniques I learned and used:

1. 📌 System Prompting

I started by defining a system prompt that sets the tone and behavior of the chatbot. Example:

You are Hitesh Choudhary, an experienced software engineer and educator. You explain complex tech topics using real-world analogies, humor, and motivation.

This alone dramatically changed the model’s response style.

2. ✍️ Writing Better Prompts

Key learnings:

• Use clear instructions

• Define roles explicitly (e.g., “Act as…”)

• Add constraints or goals (e.g., “Keep answers under 100 words.”)

• Avoid ambiguity (clarify context wherever possible)

3. 📚 Prompt Formats: Alpaca, ChatML & Inst

I experimented with different formats:

• Alpaca Prompt (used in instruction-tuned models):

  ### Instruction:
  Explain the concept of recursion.
  
  ### Response:

• ChatML (used in OpenAI chat models):

  jsonCopyEdit[
    {"role": "system", "content": "You are Hitesh Choudhary..."},
    {"role": "user", "content": "What is Docker?"}
  ]

• Inst Format (used in LLaMA-style models):

  <s>[INST] <<SYS>> You are... <</SYS>> What is Git? [/INST]

Each format has its strengths depending on the model backend.

4. 🧪 Zero-Shot Prompting

I began with zero-shot prompts — just a single user instruction. Surprisingly, this worked well for basic FAQs but lacked personality depth.

5. 🧵 Chain-of-Thought Prompting

To help the model think step by step, I used prompts like:

"Explain the concept of OAuth2. First explain what problem it solves, then describe how the flow works."

This improved the logical flow of answers significantly.

6. 🎯 Few-Shot Prompting

Finally, I added a few real examples of how the personas would respond:

### Instruction:
How should I start learning AWS?

### Response (Hitesh):
Start with IAM roles and EC2. Break things. Watch the logs. AWS is a beast, but you’ll tame it one service at a time.

This helped the chatbot "learn the style" from just a few examples.

⚙️ Tech Stack

Here’s what I used to build the chatbot:

• Backend: Python

• AI Model: OpenAI’s GPT-3.5 via API (you can easily swap it with open-source models like Mistral or LLaMA)

• Prompt Management: Custom prompt templates for each persona

• Source Code: GitHub - Persona AI Bot

🧩 Real-World Use Cases

This chatbot architecture can be extended to:

• EdTech Assistants: Emulate teachers or domain experts

• Corporate Training: Create internal knowledge agents in a company’s tone

• Customer Support: Bots that align with brand voice

• Entertainment: Chat like Sherlock Holmes, Tony Stark, or even a fictional CTO!

📉 Challenges I Faced

1. Tone Drift: Sometimes the chatbot forgot who it was. I fixed this by reinforcing persona traits in every system prompt.

2. Overfitting to Examples: Few-shot prompts made it too rigid. I balanced between dynamic and static styles.

📚 Lessons Learned

• Prompting is both an art and science — every word matters.

• Personas aren’t just about tone — they need context, values and constraints.

• Start simple. Iterate fast. Measure responses like you would any UX component.

🧑‍💻 How You Can Build Your Own

Want to build your own persona chatbot? Here’s a mini roadmap:

1. Define your persona clearly.

2. Choose an AI model (OpenAI API, Ollama, Claude).

3. Design a system prompt that sets the tone.

4. Choose between zero-shot, few-shot and chain-of-thought prompting.

5. Use open-source UI libraries like Shadcn/UI to rapidly build your frontend.

6. Add personalization and deploy 🚀

☕ Final Thoughts

Building this AI product taught me how prompting is programming — but in natural language. If you’re a developer dreaming of working with AI. You need curiosity, clarity and the courage to build something imperfect and keep improving it.

Inspired by mentors, powered by open-source and shaped by hundreds of failed prompts — this chatbot is just the beginning.

🔗 GitHub Repository

Persona AI Bot – Source Code

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