Building an AI-Powered Chatbot

In the previous lesson, we built a simple chatbot that followed predefined rules to give us the current time in various cities. It was like teaching a toddler to answer specific questions: “What time is it in Tokyo?” led to an accurate response, but ask it something slightly different, like “What’s the time in Paris?” (a city we didn’t add), and it would get confused. That gave us a taste of rule-based chatbots. It was rigid, predictable, and great for straightforward tasks. But now, it’s time to take a step forward.

What if you wanted a chatbot to help with something more dynamic, like math? Sure, we could build another rule-based bot for specific calculations like “What’s 2 + 2?” But how many rules would we need to cover all possible math problems? It would be like writing separate instructions for every grain of sand on a beach—not practical. In fact, if you actually tried to write a rule-based bot for all possible basic arithmetic questions up to just the number 100, you'd need to cover over 40,000 unique combinations! This doesn't even account for variations in how questions might be phrased in natural language, like "What's the sum of 15 and 20?" versus "Add 15 and 20 for me."

That’s where logic adapters come in. Think of them as built-in "cheat sheets" that give our chatbot advanced skills without requiring us to program every possible scenario. To demonstrate, we’ll use a chatbot that can act as a math-savvy assistant: the AI Calculator.