From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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121 lines
3.6 KiB
121 lines
3.6 KiB
WEBVTT |
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Well, day four was an information dense day. |
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I do hope that you learned some something useful here, and I hope that even those that were already |
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somewhat familiar with things like tokens and context windows picked up a thing or two and are now able |
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to more confidently put that into practice. |
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Certainly this is foundational stuff that we'll be using again and again over the next week or next |
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weeks as we build on this and apply it to commercial problems. |
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So what you should now be confident doing is writing code that calls OpenAI and also a llama and using |
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it to to summarize the summary use case that we worked on, you can now contrast the leading six frontier |
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models. |
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In fact, a bit more than that, because we've been exposed to zero one preview as well as GPT four |
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zero, and to Claude artifacts and things like that. |
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Um, and in particular, we know that almost all of them are not able to answer the question, how many |
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A's are there in the the that sentence? |
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And it's worth pointing out, of course, the reason they struggled with it. |
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Now, it should be very clear to you it's because that text is tokenized by the time it's sent in to |
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the model, and all the model knows about Is tokens. |
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And so from that perspective, counting letters doesn't mean anything to it, because all it sees is |
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the tokens that are already combined and they don't have the meaning of the letters. |
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And that's why it's actually a very difficult question for an LLM, but something like zero one preview |
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that's able to think step by step and reason and understands how things need to be spelt, is able to |
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do it. |
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Um, and then perplexity was also able to do it too, wasn't it. |
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And I suspect that's because it was able to look that up in its resources of knowledge. |
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Uh, so also now you've built on top of this to understand about the history of Transformers and how |
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we've got to where we are. |
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Tokens and what it means to tokenize context windows and how they're not just the input. |
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It's the whole conversation so far. |
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And now you know about API costs and where to go to look up the costs of APIs and the context windows |
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associated with the big models. |
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Okay. |
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Next lecture is going to be exciting. |
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You're going to be coding this time. |
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You're going to be building some confidence in your coding against the OpenAI API. |
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We're going to use a bunch of different techniques, and you're going to be implementing a business |
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solution that is more of a wholesale business solution. |
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That's going to involve a couple of different calls to LMS, and we're going to get it done in a matter |
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of minutes. |
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And it's a great lab, and it will end with exercise for you. |
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So without further ado, uh, let's wrap up for today and I will see you tomorrow for our Big Week one |
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project.
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