PSpseudocomposer·Aug 16, 2024
There's two sides to this: using AI/ML/LLMs to augment your own development ability, and using AI/ML/LLMs within apps you build.
For the former side, Copilot-type implementations are pretty intuitively useful when used this way. I find it most useful as an autocomplete, but the chatbot functionality can also be a nice, slightly-better alternative to "talk to a duck when you're stuck." That said, I'll focus on the latter side (using AI/ML in your actual work) from here.
Generalized AI/ML/LLMs are really just a "black box API" like any of the others in our toolbelt already, be they Postgres, Redis, SSE, CUDA, Rails, hell, even things like the filesystem and C atop assembly. We don't need to know all the inner workings of these things, just enough to see how to use the abstraction. You probably take when to use a lot of these things for gran
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ted at this point, but the reason we use any of these things is that they're good for the specific problem at hand. And LLMs are no different!
What's important to recognize is the types of problems that LLMs are good for, and where to integrate them into your apps. And, well, a pretty obvious class of this is parsing plain text into structured data to be used in your app. This is pretty easy to prompt an LLM to do. OpenAI and WebLLM provide a pretty straightforward common set of APIs in their NPM libraries (and other language bindings are pretty similar). It's far from a "standard," but it's definitely worthwhile to familiarize yourself with how both of these work.
For an example, I've made use of both OpenAI and WebLLM in an "Event AI" offshoot to my social media app [1], parsing social media events from plaintext (like email list content, etc.); feel free to test it and view the (AGPL) source for reference as to how I'm using both those APIs to do this.
For projects where you actually have money to spend on your LLM boxes, you'll probably do this work on the BE rather than the FE as demoed there, but the concepts should transfer pretty straightforwardly.
If you're interested in really understanding the inner workings of LLMs, I don't want to discourage you from that! But it does seem like really getting into that will ultimately mean a career change from full-stack software engineering into data science, just because both have such a broad base of underlying skills we need to have. I'm happy to be wrong about this, though!
[1] Source: https://github.com/JonLatane/jonline/blob/main/frontends/tam... | Implementation: https://jonline.io/event_ai