This repository provides a comprehensive guide to building production-ready LLM systems, covering data handling, training, retrieval-augmented generation, and deployment. It's a practical resource for engineers looking to implement real pipelines rather than just theoretical concepts.
Everyone wants to “learn AI”
but no one teaches how to build real LLM systems
This repo actually does
LLM Engineer’s Handbook
• Data → training → RAG → deployment
• Real pipelines, not just theory
• Production-ready (AWS, monitoring, CI/CD)
Basically… from zero →
Microsoft has released 7 MIT-licensed packages focused on AI agent governance, including tools for identity, policy enforcement, and trust scoring. These packages are designed for integration with existing frameworks like LangChain and AutoGen, offering low-latency performance.
Microsoft just open-sourced 7 MIT-licensed packages for AI agent governance. Identity, policy enforcement, trust scoring, OWASP coverage. Sub-0.1ms per action. Drop-in for LangChain, CrewAI, AutoGen, and more. This is the missing layer.