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kimtth/awesome-azure-openai-llm: A curated list of 🌌 Azure OpenAI, 🦙 Large Language Models (incl. RAG, Agent), and references. [code] https://github.com/kimtth/azure-openai-llm-cookbook

A comprehensive, curated collection of resources for Azure OpenAI, Large Language Models (LLMs), and their applications.

This repository serves as a comprehensive guide to the rapidly evolving field of LLMs and Azure OpenAI services. Key features:

🔹Concise Summaries: Each resource is briefly described for quick understanding
🔹Chronological Organization: Resources ordered by date (first commit, publication, or paper release)
🔹Active Tracking: Regular updates to capture the latest developments

Note: Some content may become outdated due to the rapid pace of development in this field.

Retrieval-Augmented Generation - Enhancing LLMs with External Knowledge

• RAG Fundamentals - Core concepts and implementation strategies
• RAG Architecture Design - System design patterns and best practices
• RAG Applications - Real-world implementations and use cases
  • GraphRAG - Graph-based retrieval approaches
• Vector Databases - Comparison and selection guide

Microsoft's Cloud-Based AI Platform and Services

• Microsoft LLM Framework - Official frameworks and SDKs
• Microsoft Copilot - Copilot products overview
• Azure AI Services - Azure AI Search, AI services
• Microsoft Research - Research publications and findings
• Reference Architectures - Proven architectural patterns and samples

Building Real-World Applications with Large Language Models

• Development Frameworks - Tools for building LLM applications
• Application Development - Implementation guides and best practices
  • Code Development Tools - AI-powered coding assistants and editors
  • Memory Systems - Persistent memory and context management
• Performance Optimization - Caching strategies and UX improvements
• Emerging Concepts - New paradigms like Vibe Coding and Context Engineering
• Robotics Integration - LLMs in robotic systems
• Demonstration Projects - Inspiring examples and showcases

Building Autonomous AI Agents and Multi-Agent Systems

• Design Patterns - Proven architectural approaches for agent systems
• Development Frameworks - Tools and libraries for building agents
• Agent Applications - Real-world agent implementations
  • Code Interpreters - Open-source alternatives to OpenAI's Code Interpreter
  • Model Context Protocol - MCP, Agent-to-Agent communication, and computer interaction
  • Research Agents - AI systems for deep research and analysis

Optimizing Model Performance and Behavior

• Prompt Engineering - Techniques for effective prompt design
• Model Finetuning - PEFT (LoRA), RLHF, and supervised fine-tuning
• Model Optimization - Quantization and performance optimization
• Advanced Techniques - Mixture of Experts (MoE) and other patterns
• Visual Prompting - Working with multimodal inputs

Understanding LLM Capabilities and Limitations

• AGI and Social Impact - Discussions on artificial general intelligence
• OpenAI Ecosystem - Product roadmaps and strategic direction
• Technical Constraints - Context limitations and solutions (e.g., RoPE)
• Safety and Security - Building trustworthy AI systems
• LLM Capabilities - Understanding what LLMs can and cannot do
  • Reasoning Abilities - Logical reasoning and problem-solving

Overview of Available Models and Technologies

• Model Taxonomy - Classification and comparison of LLMs
• Model Collection - Comprehensive list of available models
• Domain-Specific Models - Specialized models for software development and other domains
• Multimodal Models - Models handling text, image, audio, and video

Microsoft's Orchestration Framework and Optimization Tools

• Semantic Kernel - Microsoft's micro-orchestration framework for AI applications
• DSPy - Optimizer frameworks for systematic prompt and model optimization

Popular Open-Source Frameworks for LLM Applications

• LangChain Features - Comprehensive feature overview and cheat sheets
• LangChain Agents - Agent implementations and critical analysis
• Framework Comparisons - LangChain vs. alternative frameworks
• LlamaIndex - Micro-orchestration and RAG-focused framework

Practical Tools and Browser Extensions

• AI Tools & Extensions

Training and Evaluation Data Resources

• Training Datasets - High-quality datasets for model training and fine-tuning

Measuring and Improving LLM Performance

• Evaluation Frameworks - Methods and metrics for LLM assessment
• LLMOps - Operations and lifecycle management for LLM systems

Comprehensive Surveys and Learning Materials

• LLM Surveys - Academic surveys and systematic reviews
• Business Applications - Industry use cases and implementation strategies
• Building from Scratch - Educational resources for understanding LLM internals
• Multilingual Resources - LLM resources for Korean, Japanese, and other languages
• Learning Materials - Tutorials, courses, and supplementary resources

Last Updated: July 23, 2025

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