Popular GitHub Repositories for Representation Engineering

21 Lessons, Get Started Building with Generative AI ? https://microsoft.github.io/generative-ai-for-beginners/

? Guides, papers, lecture, notebooks and resources for prompt engineering

Learn how to design, develop, deploy and iterate on production-grade ML applications.

? Papers & tech blogs by companies sharing their work on data science & machine learning in production.

This repository is primarily maintained by Omar Santos (@santosomar) and includes thousands of resources related to ethical hacking, bug bounties, digital forensics and incident response (DFIR), artificial intelligence security, vulnerability research, exploit development, reverse engineering, and more.

Turns Data and AI algorithms into production-ready web applications in no time.

Machine Learning Engineering Open Book

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

? Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with OpenTelemetry, Langchain, OpenAI SDK, LiteLLM, and more. ?YC W23

A curated list of references for MLOps

Structured Text Generation

Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.

ChatGPT 中文指南?，ChatGPT 中文调教指南，指令指南，应用开发指南，精选资源清单，更好的使用 chatGPT 让你的生产力 up up up! ?

Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.

LangGPT: Empowering everyone to become a prompt expert!? Structured Prompt，Language of GPT, 结构化提示词，结构化Prompt

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

A collection of GPT system prompts and various prompt injection/leaking knowledge.

What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?

所有系统提示都需透明公开