An awesome & curated list of resources for building products around LLMs, and machine learning models in general.
"There’s a large class of problems that are easy to imagine and build demos for, but extremely hard to make products out of. For example, self-driving: It’s easy to demo a car self-driving around a block; making it into a product takes a decade." - Andrej Karpathy
In the past few years, we witnessed a high hype towards LLM-powered products. Pioneers, visionaries and copycats. Beyond the hype, practical insights on applying AI effectively are accumulating, revealing patterns and challenges in real-world implementation.
So, I decided to collect the applied LLM content I like. Feel free to contribute!
What We’ve Learned From A Year of Building with LLMs June, 2024 A comprehensive guide on LLM engineering. Must-read.
Patterns for Building LLM-based Systems & Products July, 2023 From one of the authors of "What We've Learned From A Year of Building with LLMs", Eugene Yan. Presents extremely valuable insights.
LLM Engineer's Handbook by Paul Lusztin and Maxime Labonne October, 2024
AI Engineering by Chip Huyen Work in Progress, Late 2024
Hands-on Large Language Models by Jay Alammar and Maarten Grootendorst October, 2024
Designing Machine Learning Systems by Chip Huyen June, 2022 MLOps questions are also LLMOps questions.
Educational Resources - Parlance
Build With LLMs: AI Engineering Patterns for Scrappy Developers
Prompting Fundamentals and How to Apply Them Effectively Short but comprehensive review by Eugene Yan, must-read.
Prompt Engineering Short but comprehensive review by Lilian Weng, must-read.
Prompt Engineering Guide by dair.ai Extensive resource on prompt engineering.