Building LLM applications for production

[Hacker News discussion, LinkedIn discussion, Twitter thread]

[ Hacker News discussion , LinkedIn discussion , Twitter thread ] Update: My upcoming book, AI Engineering (late 2024/early 2025) will cover building aplications with foundation models in depth. A question that I’ve been asked a lot recently is how large language models (LLMs) will change machine learning workflows. After working with several companies who are working with LLM applications and personally going down a rabbit hole building my applications, I realized two things: It’s easy to make something cool with LLMs, but very hard to make something production-ready with them. LLM limitation

related reading

• What We’ve Learned From A Year of Building with LLMs – Applied LLMsapplied-llms.org
• Patterns for Building LLM-based Systems & Productseugeneyan.com
• Productizing Large Language Modelsblog.replit.com
• Malleable software in the age of LLMsgeoffreylitt.com
• LLM Powered Autonomous Agents | Lil'Loglilianweng.github.io
• GitHub - brexhq/prompt-engineering: Tips and tricks for working with Large Language Models like OpenAI's GPT-4.github.com
• Building Effective AI Agents \ Anthropicanthropic.com
• Here’s how I use LLMs to help me write codesimonwillison.net
• Your AI Product Needs Evals – Hamel's Blog - Hamel Husainhamel.dev
• Prompt Engineering Guide | Prompt Engineering Guidepromptingguide.ai
• The biggest bottleneck for large language model startups is UX | by Davis Treybig | Innovation Endeavors | Mediummedium.com
• Evaluating LLM Applicationshumanloop.com