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kimtth azure-openai-llm-notes

A curated collection of resources for 🌌 Azure OpenAI, 🦙 LLMs (+RAG, Agents). Monthly Updates.

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  • community 70

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agent agent-framework agent-skills ai-engineering ai-tools awesome azure azure-openai claude coding-agent harness llm llm-wiki llmops openai prompt-engineering rag research-paper survey-paper Python OpenAI Azure Claude
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README.md
View README on GitHub

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.

🧭 Quick Navigation (Propedia-style)

Layer / Era What it controls Jump to sections
Weights
2022-2023		 Parametric knowledge baked into the model.
Themes: Pretraining, Scaling Laws, Fine-tuning, RLHF, Alignment, Instruction-following, Few-shot		 Foundations: Large Language Model Landscape, Large Language Model Collection, Foundation Model Providers
Training: Large Language Model Training and Optimization, Model Training & Inference, Training & Fine-tuning
Behavior and safety: Trust, Safety, and Security, Safety, Security & LLMOps
Context
2023-2024		 What the model sees at inference time.
Themes: Prompting, Chain-of-Thought, RAG, Memory, Long Context, Knowledge Injection, Context Engineering		 Prompting: Prompt Engineering and Visual Prompts, Prompt Engineering & Tooling
Retrieval: RAG, Azure AI Search, RAG Best Practices
Memory and context windows: Context and Long-Context Limits, Memory, Data Processing & Memory
Agentic Engineering
2025-2026		 How agents act, self-correct, and coordinate in the real world.
Themes: Harness Engineering, Loop Engineering, Graph Engineering, Function Calling, Tool Ecosystems, MCP, Skills, Multi-agent, A2A protocols, Orchestration, Agent Infrastructure, Security		 Agent runtime: AI Application, Agent Frameworks, Agent Development, Agent Best Practices
Protocols and tools: Agent Protocol, Coding & Research, Skills, Agentic Engineering, Dev Tools, MCP & Extensions
Apps and operations: Evaluating Large Language Models, LLMOps, Learning Resources & Workshops, Code Samples & Workshops

Refereces: DailyDoseOfDS - Evolution of the Agent Landscape

1. App & Agent

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

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2. Azure OpenAI & Copilot

🌌 Microsoft's Cloud-Based AI Platform and Services

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3. Research & Survey

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

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4. Datasets, Evaluation, and Extras

🛠️ Training Data, Datasets & Evaluation Methods

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5. Best Practices

📋 Curated Blogs, Patterns, and Implementation Guidelines

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🧭 Start Here

Category Goal Suggested path
RAG Explore RAG patterns RAG → GraphRAG → RAG Application → RAG Best Practices → RAG Research
AI Engineering Build an AI Engineering Workflow RAG → AI Application → Agent Protocol → Coding → Deep Research → Domain-Specific Agents → Skills → Agentic Engineering
Agentic Engineering Extend a coding agent Skills → Agentic Engineering → Coding → Tool Use → Evaluation Metrics
Agents Design an agent workflow Top Agent Frameworks → Agent Design Patterns → Tool Use → Memory → Agent Research
Data & Analytics Build a data or analytics agent Data & Analytics Agents → Data Processing & OCR → Memory → Tool Use → Evaluating Large Language Models
Local LLMs Build a local or self-hosted LLM application Large Language Model Collection → Model Serving & Local Runtimes → Model Gateway → UI & No-Code Tool → Observability & LLMOps
MCP & Integration Build MCP-enabled tools Model Context Protocol → Dev Tools, MCP & Extensions → Safety, Security & LLMOps → Agent Best Practices
Developer Agents Build coding or research agents Coding → Deep Research → Skills → Agentic Engineering → Tool Calling & Agentic
Azure / RAG Build an Azure RAG application Azure OpenAI & Foundry Overview → Azure AI Search → RAG Solution Design → Sample Applications → Evaluating Large Language Models
Azure / Agents Build an Azure agent Agent Frameworks → Agent Design Patterns → Model Context Protocol → Agent Development → Evaluating Large Language Models
Microsoft 365 Build a Microsoft 365 agent Microsoft 365 Agent Development → Copilot Product Catalog → Dev Tools, MCP & Extensions → Agent Development
Production Operate an AI application in production Architecture Patterns & Use Cases → Safety, Security & LLMOps → LLMOps → Evaluating Large Language Models
Research Learn the LLM landscape Large Language Model Landscape → Survey and Reference → LLM Research
Model Development Train or fine-tune a model Large Language Model Collection → Model Training & Inference → Training & Fine-tuning → Datasets for LLM Training → Evaluating Large Language Models
Multimodal Build a multimodal application Multimodal Models → Data Processing & OCR → RAG Application → Vision & Multimodal
Evaluation Choose and benchmark a model Large Language Model Collection → Architecture Comparisons → Evaluating Large Language Models → LLM Evaluation Benchmarks → Evaluation Metrics

📖 Legend & Notation

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

Info: Applications that have been archived or have had no commits for more than 12 months are listed in applications.old.md.

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Created: Apr 13, 2023

Last push: Aug 31, 2026

Default branch: main

Top contributors

Languages

Share of the codebase by language, based on repository metadata from the host.

  • Python 100.0%

Repository Radar analysis

Deterministic insights derived from public metadata and our observations — not personal testing or reviews.

Why this repository is interesting

  • Maintained recently (last push 3 weeks ago).

Who should use it

  • Developers working primarily with Python
  • Teams exploring AI tooling, agents, or ML infrastructure

Potential use cases

  • Reference or evaluate Python open-source approaches in this domain
  • Prototype AI/agent workflows or study reference architectures

Strengths

  • Recent repository activity
  • README present in our index

Limitations / considerations

  • License not recorded in our index — confirm on GitHub before production use

What to watch

  • Re-check last push, issues, and releases on GitHub before production adoption

Strong signals: Active maintenance

Source: GitHub (public metadata) + Repository Radar analysis. We do not claim ownership of third-party repositories.

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