The jump from Eliza to ChatGPT is not just a better chatbot — it is a completely different mechanism, because earlier chatbots were rule-based systems where every response had to be programmed in advance by a human.
CJ distinguishes rule-based chatbots (Eliza, Perry, ALICE, SmarterChild) from LLMs by mechanism, not just capability: one is hand-coded if-statements, the other is learned from data. ✦ AI generated
CJ · Syntax · 2026-07-10 · original ↗
starts at this moment · 6:09
Every single one of these chatbots up until this point were based on rules or scripts or templates or decision trees. Essentially, a human had to sit down at a computer and program every single response in advance. So, the jump from Eliza to chat GPT isn't just a better chatbot, it's a completely different mechanism.
verbatim transcript · starts at 6:09
6:05subject and then respond with, "Tell me more about your subject." In this case, my subject is boss. So, as you can see, the code is super straightforward, but if you didn't know what was going on behind the scenes, you might think that there was something smart on the other side. And beyond these matchers, we also have catch-all responses. So, these are just random continuations like, "Please go on. Tell
6:26me more about that. How does that make you feel?" So, if I say something that we didn't match against, like, "He is cool and is a breakdancer." It will respond with, "Please go on." So, we didn't have any direct matchers for that particular sentence. It picked a random continuation and now the user can keep chatting with the bot. So, with just a few if statements, we can make the user
6:45feel like there's something smart on the other side. And that's the basics of this chatbot. So, we built a super simple chatbot. It's just a bunch of if statements, but the UI that the user is interacting with is almost exactly the same as ChatGPT or Claude. I mean, I mean, it is, right? It has an input box. The user types, it gets back some response. And I like to kind of abstract
7:02this and think about it as as a black box, right? It has some input, performs some process, and then gives some output. In this case, the process is just run the input through a bunch of if statements. But what's interesting about this is we can actually replace that box with an LLM, right? Prompt goes in, LLM does something, answer comes out. And that's essentially what we're going to
7:23do throughout this video. We're going to slowly replace the pieces to get smarter and smarter machinery. And I actually like to think about the world of computing and technology in this way. Everything initially is a black box, but we can start to uncover how that thing works. And if we generalize it as inputs and outputs, we can even replace the black box with smarter machinery later on. Now, before you can prompt an LLM,
7:43that LLM needs to be created, right? When you type a question into ChatGPT, that gets sent to a pre-trained model. So, that training process has to happen ahead of time. And essentially, that training process produces a model file that then lives on a server somewhere, and that's actually what we're interacting with. And I'm already getting ahead of myself because first, we need to talk about what is a model.
8:04Now, LLM stands for large language model. It's actually crazy that I haven't defined that this far into the video, but it has the word model in there, and a model is a neural network, which is essentially a system of interconnected nodes called neurons that are organized into layers. And data flows through the input layer, passes through one or more hidden layers, and comes out through the output layer. And each connection has a