Lesson 5: Build a Custom AI Chatbot Project

In this final project workshop, you will put everything you have learned about Large Language Models, prompting, and developer APIs to use by building a custom context-aware AI Assistant!

Project Overview: Build a Custom AI Chatbot

Your goal is to build an interactive terminal chatbot in Python using the Google Generative AI library. The chatbot will act as a specialized coding tutor that enforces system-prompt behaviors and maintains conversational context (memory) across multi-turn exchanges.

Initializing Chat Sessions

Rather than manually appending user and assistant history arrays for every prompt, the Gemini SDK provides a convenient chat utility. You start a chat session by callingmodel.start_chat(), optionally passing systemInstruction to lock down behavior, and send messages dynamically with chat.sendMessage():

# Starting a stateful chat session
chat = model.start_chat(
    history=[],
    system_instruction="You are a strict Python mentor."
)

# Multi-turn interaction
response1 = chat.send_message("What is a list?")
response2 = chat.send_message("Can you give me a coding example?")

Project Tasks

Implement the following architectural tasks inside the code panel to build your chatbot script:

[ ]Task 1: Define a systemInstruction that restricts the bot to explain coding tasks only.
[ ]Task 2: Initialize a Gemini client model and configure it with the custom system instruction parameter.
[ ]Task 3: Call start_chat() to create a stateful conversation object.
[ ]Task 4: Write a loop that accepts input, invokes chat.sendMessage(), prints responses, and handles exits.