azure-openai-llm-notes

Azure OpenAI + LLM

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

🔹Concise Summaries: Each resource is briefly described for quick understanding
🔹Chronological Organization: Resources appended with date (first commit, publication, or paper release)
🔹Monthly Updates: The list is updated monthly; candidate entries before the update are tracked in the issue.
🔹A list for Azure and Microsoft products only, linted by the official Awesome rules: Azure OpenAI & Copilot.
🔹Applications archived or inactive for over 12 months are listed in applications.old.md. Archived Azure repositories are listed in azure.old.md.

🧭 Quick Navigation (Propedia-style)

Layer / Era	What it controls	Jump to sections
Weights 2022-2023	Capabilities encoded in model parameters. Themes: Pretraining, scaling, fine-tuning, alignment	Model landscape · Model collection · Training & optimization · Model training
Context 2023-2024	Instructions and knowledge supplied at inference. Themes: Prompting, RAG, memory, long context	Prompting · RAG · IQ, Data & Memory (Azure) · Memory · Long-context limits
Agentic Engineering 2025-2026	How applications use models and tools to act. Themes: Agents, MCP, skills, orchestration, evaluation	Agent frameworks · Agent protocols · Agentic engineering · Agent best practices · Evaluation

Refereces: DailyDoseOfDS - Evolution of the Agent Landscape

1. App & Agent

🚀 RAG Systems, LLM Applications, Agents, Frameworks & Orchestration

• RAG
  • RAG
  • GraphRAG
  • RAG Application
  • Vector Database & Embedding
• Application
  • Top Agent Frameworks
  • Additional Agent Framework
  • Cache
  • Data & Analytics Agents
  • Data Processing & OCR
  • Desktop AI Assistant
  • Memory
  • Model Gateway
  • Model Serving & Local Runtimes
  • Observability & LLMOps
  • Popular LLM Applications (GitHub Stars >= 1000)
  • SDKs, Integration & ML Libraries
  • Training & Fine-tuning
  • UI & No-Code Tool
• Agent Protocols
  • A2A
  • Computer & Browser use
  • Model Context Protocol (MCP)
• Coding & Research
  • Coding
  • Deep Research
  • Domain-Specific Agents
  • Skills
  • Agentic Engineering: Harness Engineering → Loop Engineering → Graph Engineering

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2. Azure OpenAI & Copilot

🌌 Microsoft's Cloud-Based AI Platform and Services

• Microsoft Ecosystem
  • Microsoft 365 Copilot
  • Power Platform & Copilot Studio
  • Copilot Products
• Platform
  • AI Platform & Services
  • Models, Training & Inference
  • IQ, Data & Memory
  • Agents & Context Engineering
• Operations & Design
  • Developer Tools, MCP & .NET
  • Safety, Security & LLMOps
  • Architecture & Reference Designs
  • Solution Accelerators & Samples
  • Use Cases & Customer Stories
• Learning & Research
  • Learning Resources & Workshops
  • Microsoft Research

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3. Research & Survey

🧠 LLM Landscape, Prompt Engineering, Finetuning, Challenges & Surveys

• Landscape
  • Large Language Model Landscape
  • Large Language Model Collection
  • Foundation Model Providers
  • Domain-Specific and Specialized LLMs
• Prompting
  • Prompt Engineering and Visual Prompts
• Training & Optimization
  • Large Language Model Training and Optimization
  • Pre-training and Data Preparation
  • Architecture and Inference Patterns
  • Capabilities and Reasoning
  • Post-training and Fine-Tuning
• Impact & Products
  • AI Adoption, Impact, and Society
  • OpenAI Products
  • Anthropic AI Products
  • Google AI Products
• Survey & Reference
  • Survey and Reference
  • Additional Topics: A Survey of LLMs
  • LLM Research (Ranked by cite count ≥150)
  • Learning Resources, Implementations, and Regional Materials

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4. Datasets, Evaluation, and Extras

🛠️ Training Data, Datasets & Evaluation Methods

• Data
  • Datasets for LLM Training
• Evaluation
  • Evaluating Large Language Models
  • LLM Evaluation Benchmarks
  • LLMOps: Large Language Model Operations
• Extras
  • LLM for Robotics
  • Awesome Demo

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5. Best Practices

📋 Curated Blogs, Patterns, and Implementation Guidelines

• RAG
  • The Problem with RAG
  • RAG Solution Design
  • RAG Research
  • RAG Research (Ranked by cite count >=100)
• Agent
  • Agent Design Patterns
  • Agent Research
  • Agent Research (Ranked by cite count >=100)
  • Tool Use: LLM to Master APIs
• Security
  • Security and Governance
• Reference
  • Proposals & Glossary

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🧭 Start Here: Choose a Goal

Pick the outcome closest to your task and follow the links in order. Each path is a short starting point, not a required sequence.

Goal	Suggested path
Build a RAG application	RAG → Azure IQ, Data & Memory (AI Search) → RAG Solution Design → RAG Application
Design an AI agent	Top Agent Frameworks → Agent Design Patterns → Agents & Context Engineering → Memory
Connect tools with MCP	Model Context Protocol → Developer Tools, MCP & .NET → Tool Use
Build a Microsoft 365 agent	Microsoft 365 Copilot → Agents & Context Engineering
Build a coding or research agent	Coding → Skills → Agentic Engineering → Deep Research
Run a local model application	Model Collection → Model Serving & Local Runtimes → Model Gateway → Observability & LLMOps
Train or evaluate a model	Datasets for LLM Training → Training & Fine-tuning → Evaluating Large Language Models → Evaluation Metrics
Operate an application in production	Architecture & Reference Designs → Safety, Security & LLMOps → LLMOps → Evaluation
Explore LLM research	Model Landscape → Survey and Reference → LLM Research

📖 Legend & Notation

Symbol	Meaning	Symbol	Meaning
	GitHub repository	🗄️	Archived files
💡🏆	Recommend	📺	Video content
📑	Academic paper	🤗	Huggingface

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