From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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202 lines
5.8 KiB
202 lines
5.8 KiB
WEBVTT |
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And welcome back everybody. |
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Welcome to week two day three. |
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It's a continuation of our enjoyment of radio, our celebration of everything that is radio and user |
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interfaces. |
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Uh, what you can already do in addition to using open AI, anthropic and Gemini, you can now also |
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build UIs for your solutions. |
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And you should feel pretty good about that. |
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Uh, by the end of today, you'll be able to do more. |
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You'll be able to build chat UIs, a specific type of UI which is very common. |
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You'll be able to provide the history of conversation in a prompt, and you will build your very first |
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customer support assistant, an AI assistant, also known as a chat bot. |
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A very common I use case. |
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You will have mastered it today. |
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So again, very common. |
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J'en ai use case. |
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I think we're all very familiar with them. |
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Llms based on chat bots. |
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Super effective at conversation. |
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It's hard to remember that only a few years ago, if you experienced one of these chatbot style interfaces |
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on a website, you would be in the world of responding one, two, three, or four to different things, |
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or use a keyword like booking or something like that. |
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How far we have come. |
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You can now have an informed conversation with customer service chatbots on websites, and you often |
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do. |
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And, you know, frankly, there have been times when I've got more value from a conversation with a |
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chatbot than I have from a human being, which is a sorry, sad sense of the times. |
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Um, but obviously we can't do things like asking it how many times the letter A appears in that sentence. |
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Uh, but anyways, uh, the, uh, the chatbot use case. |
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Very familiar, very important indeed. |
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And something where llms excel. |
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You can imagine some of the things that we're familiar with, the friendly personas that we can give |
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chatbots, or indeed any persona we can have the ability to maintain context between messages this staggering |
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way that you can hold a conversation and refer to things that you said earlier. |
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And we all know now that that is some, some, some trickery going on there. |
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It's an illusion that you're really having this persistent conversation. |
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What's happening is at each step. |
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The entire conversation history is being provided to the LLM in order to get back the next response. |
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Um, and then also these assistants can have subject matter expertise, which they use to answer questions |
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in a knowledgeable way. |
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So, uh, very important aspect of interacting with assistants is the correct use of prompts. |
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We're very familiar now with the system prompt that we can use to set the tone of the conversation. |
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You can establish ground rules. |
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There is a common prompt technique of saying if you don't know the answer, just say so. |
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To try and encourage llms to be truthful and not to hallucinate. |
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Uh, context is how you can use, uh, the add additional information into the conversation to give |
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the LLM more context on what's being discussed. |
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And then multi shots prompting is when you add information to the prompt to give multiple examples of |
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interactions as a way to, uh, craft, to sort of hone the character of the LLM by giving it examples |
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to work from, and also to prime it with information that might be useful later. |
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It's interesting that this feels a bit like training because it's learning from multiple examples, |
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but of course, this isn't training in the data science sense. |
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The model has already been trained. |
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The neural network training has happened. |
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This is all at what we call an inference time at runtime. |
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It's all just generating future tokens based on past. |
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But the point is that if that past set of tokens includes a bunch of questions and answers, then when |
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it's predicting the future, it's more likely it's more likely to pick future tokens that are consistent |
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with what it's seen in the past. |
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And that's why this works so very well. |
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So we're now going to build a chatbot. |
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Our first chatbot. |
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And it's going to look like this. |
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It's going to have a sort of instant message style interface to it with questions from us, responses |
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from the chatbot in this sort of interface, which, you know, that's that's reasonably sophisticated |
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and I'm telling you that we're going to be able to do it all in this one lesson, and it will give you |
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tooling to be able to do the same thing in the future. |
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So without further ado, let's go over to JupyterLab.
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