Building a chatbot for your campus club as a first project

This project is small enough to finish in a weekend and real enough to impress. Here is why it works as a first project:

Clear scope. The chatbot does one thing: answer questions about your club. The scope is naturally limited, which means you can actually finish it instead of building forever.

Real users. Your club members can use it. Real users give real feedback, which teaches you more than building something nobody touches.

Covers key AI concepts. You learn how to structure a system prompt, how to constrain AI responses to relevant topics, and how to handle edge cases (what happens when someone asks something unrelated). These are the same concepts used in professional AI products.

Portfolio value. A deployed chatbot with a link you can share is a strong portfolio piece. It shows you can build with AI, deploy a product, and solve a real problem. That is what makes a project real.

A laptop with Python or Node.js installed. Either language works. Use whichever you already know. If you know neither, Python is simpler to set up for this project.

An AI API key. Sign up for OpenAI, Google Gemini, or Anthropic. All offer free tiers or credits for new accounts. You will use the chat completions endpoint to send questions and receive answers.

Your club information. Gather everything a new member might ask: meeting times, location, how to join, contact person, upcoming events, membership fees (if any), social media links. Put this in a text file. This becomes your chatbot's knowledge base.

A deployment account. Vercel (for JavaScript), Streamlit Cloud (for Python), or Replit (for either). All are free for small projects.

Step 1: Write the system prompt. This is the instruction that tells the AI how to behave. Example: "You are the FAQ assistant for the [Club Name] at [University]. Answer questions about the club using only the information provided below. If someone asks something you do not have information about, say you do not know and suggest they contact the club chair at [phone/email]. Do not make up information." Then paste your club information below the instruction.

Step 2: Build the API call. Write a function that takes a user question, combines it with the system prompt, sends it to the AI API, and returns the response. In Python with the OpenAI library, this is about fifteen lines of code.

Step 3: Add a simple interface. For Python, use Streamlit: a text input box, a submit button, and a response area. For JavaScript, build a simple HTML page with a form and a fetch call to your backend. The interface does not need to be fancy. It needs to work.

Step 4: Test with real questions. Ask the chatbot every question you can think of. "When do you meet?" "How do I join?" "What programming languages do you teach?" Also test edge cases: "What is the weather?" or "Do my homework for me." Make sure the bot stays on topic and does not invent information.

Step 5: Deploy. Push to GitHub and deploy to your chosen platform. Share the link with your club WhatsApp group and ask members to try it. Collect feedback and improve.

Version one works but is basic. Here are improvements you can add over the next few weeks:

Add conversation history. Track previous messages in the session so the bot can handle follow-up questions like "What about Saturdays?" after asking about meeting times.

Add a WhatsApp integration. If your club communicates via WhatsApp, connect the chatbot to a WhatsApp Business API. This makes it accessible where your members already are. Libraries like Twilio or the WhatsApp Cloud API make this possible.

Track common questions. Log the questions people ask (without personal data). This tells you what information is missing or unclear, and helps you improve the knowledge base.

Add event updates. Connect the bot to a simple data source (a Google Sheet or a JSON file) that club leaders update with new events. The bot always has current information without you editing the code.

Each improvement teaches a new skill: session management, third-party integrations, logging, data sources. By version three, you have a substantial AI project on your portfolio.