From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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112 lines
3.6 KiB
112 lines
3.6 KiB
WEBVTT |
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Let me talk about some other phenomena that have happened over the last few years. |
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One of them has been the rise and fall, perhaps of a new type of job called the Prompt engineer, someone |
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who specializes in knowing how to craft the right kind of prompts to get the best outcomes from llms. |
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At one point, this role commanded a $500,000 salary and was hot in demand. |
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It does seem to have fallen downwards a bit now. |
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The demand is less, partly because knowing how to prompt well has become ubiquitous. |
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There's so much content now about the right ways to go about prompting, and also because there are |
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now tools that will actually create a prompt for you. |
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Anthropic, in fact, has has one of those tools. |
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So it is now something that has become relatively common. |
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Another another phenomenon was the custom gpts. |
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OpenAI has a GPT store that was incredibly popular for a while. |
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It's become a little bit saturated at this point. |
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I think people became fatigued of building custom GPT, gpts, but it's still there. |
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The GPT store is still reasonably popular, and you can go there to experiment with different kinds |
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of tuned gpts. |
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Then of course, still very important was the emergence of co-pilots ways in which a human and an LLM |
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could collaborate together. |
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Famously, Microsoft Copilot, I think, was perhaps the first one that really took the world by storm. |
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GitHub copilot of course, and there are many more co-pilots that are being embedded into a lot of applications. |
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And in a way, we sort of saw that with canvas a moment ago. |
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And then the new hot trend right now is all about agent ization, about using Agentic AI, which is |
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where multiple llms collaborate to solve a problem. |
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A more complex problem is broken down into smaller steps or smaller tasks, and then particularly tuned |
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llms are used to tackle each of those steps, perhaps also with an LM responsible for planning and deciding |
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which LM is doing what. |
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Also, with a concept of memory, that there's some kind of persistent information that lasts, that |
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can be exchanged between the LMS and a sense of autonomy, that the LMS don't just exist for the purposes |
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of a chat interface with a human, but they have a sort of time horizon that that spans multiple chats, |
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potentially. |
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So that sense of autonomy and memory and being able to plan tasks and divide tasks down, those are |
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all some of the core tenants of Agentic AI. |
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And we'll be coming back to this a few times during the course, but in particular at the end of the |
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course in week eight, we will build a full Agentic AI solution, as I might have said, a time or two, |
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but it's really great and it will have eight, seven, seven. |
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I don't get overexcited. |
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There are seven agents that will collaborate as part of what we will build at the end.
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