As an experienced LLM user, I don't use generative LLMs often

Lately, I’ve been working on codifying a personal ethics statement about my stances on generative AI as I have been very critical about several aspects of modern GenAI, and yet I participate in it. While working on that statement, I’ve been introspecting on how I myself have been utilizing large language models for both my professional work as a Senior Data Scientist at BuzzFeed and for my personal work blogging and writing open-source software. For about a decade, I’ve been researching and developing tooling around text generation from char-rnns, to the ability to fine-tune GPT-2, to experiments with GPT-3, and even more experiments with ChatGPT and other LLM APIs. Although I don’t claim to the best user of modern LLMs out there, I’ve had plenty of experience working against the cons of next-token predictor models and have become very good at finding the pros. It turns out, to my surprise, that I don’t use them nearly as often as people think engineers do, but that doesn’t mean LLMs ... Read full article.