Microsoft Developer
6 HN recommenders · recommended in 7 months, 2023–2025
42:40 · 786K views · May 25, 2023
Andrej Karpathy walks through how GPT assistants are trained, from tokenization and pretraining to supervised finetuning and RLHF, and gives practical prompting strategies and a view of the tooling ecosystem.
Recommended as the clearest overview of how LLM assistants are built and how to use them well.
11 comments from Hacker News
I would highly recommend watching - State of GPT video by Karpathy at Microsoft Build to get an idea of how LLM’s work. There are some really good prompting ideas. It changed the way I use GPT-4. Video: this video
I recognize there's plenty of catnip here when it comes to calling this "engineering" or not, however, whatever you want to call it (prompt fiddling?), the techniques are crucial if you want to achieve reasonably consistent output from current-state LLMs. As models improve concerns about context window limitations will be reduced and it will be easier to discern user intent. These are good straight-to-the-point guides: - Prompt Engineering by BrexHQ: [LINK] - OpenAI guidance: [LINK] - [LINK] - (great examples): [LINK] - (~hr) Karpathy talk: this video tl;dr: - Begin with the best and most forgiving model available, optimizing it later if necessary. - Write your prompts in a clear, specific, and detailed manner - Be mindful, however, of the tradeoff between including more detail and increasing latency or cost. - If you're doing any complex reasoning, ask the model to show its work, help "stretch out" computation over more tokens - "Escape" any included or quoted text properly to avoid confusing the model. - Keep track of your token budget, considering the context window in conversations and response length. - Employ an iterative process of measuring, adjusting, and improving your prompt engineering techniques.
I share many of your concerns and frustrations, although I suspect what you're asking for their consider a moat along the lines of a trade secret, rivaled only by the collection of performance improvement techniques they've amassed in 1000s-10,000s of training runs, 100s of engineers, and (hundreds of?) millions spent on compute. People are hired and praised in the community for their skill in cleaning data. A non-answer for you, but for curious others, [State of GPT] 10 days ago provides a through introduction to the process used to train, Karpathy speaking at a Microsoft event providing a deep summary review of concepts, training phases, and techniques proving useful in the world of Generative Pretrained Transformers. [State of GPT]: this video
It sounds like you want more broad stuff, not necessarily learning how to train models. More like learning to use them and how they work. [LINK] and Hacker's Guide to LLMs by Jeremy from Fast.ai - [LINK] State of GPT by Karpathy - this video LLMs by 3b1b - [LINK] Visualizing transformers by 3b1b - [LINK] How ChatGPT trained - [LINK] AI in a nutshell - [LINK] How Carlini uses LLMs - [LINK] For staying updated: X/Twitter & Bluesky. Go and follow people that work at OpenAI, Anthropic, Google DeepMind, and xAI. Podcasts: No Priors, Generally Intelligent, Dwarkesh Patel, Sequoia's "Training Data"
Karpathy explains it far better than I did in his "State of GPT" talk about a month ago: this video Yes, language can do that, but books and other texts, on average, leave out a lot of steps that humans have learned to do naturally. They can be prompted to do them, and their answers improve, indicates that there is definitely some level of "reasoning" there. But it has weird limitations in its current forms. I expect that to improve pretty quickly, tbh.