Awesome Azure Openai Llm Overview

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

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Azure OpenAI + LLM

Curated resources on Azure OpenAI, Large Language Models (LLMs), and applications.

🔹Brief each item on a few lines as possible.
🔹Capturing a chronicle and key terms of that rapidly advancing field, ordered by date.
🔹The dates are based on the first commit, article publication, or paper version 1 issuance.
🔹GitHub links (with star counts); others are articles or papers.
🔹Disclaimer: Please be aware that some content may be outdated.

Contents

• Section 1: RAG
• Section 2: Azure OpenAI
• Section 3: LLM Applications
• Section 4: Agent
• Section 5: Semantic Kernel & DSPy
• Section 6: LangChain & LlamaIndex
• Section 7: Prompting & Finetuning
• Section 8: Challenges & Abilities
• Section 9: LLM Landscape
• Section 10: Surveys & References
• Section 11: AI Tools & Extensions
• Section 12: Datasets
• Section 13: Evaluations

Section 1 🎯: RAG

• RAG (Retrieval-Augmented Generation)
• RAG Design
• RAG Application
  • GraphRAG
• Vector DB

Section 2 🌌: Azure OpenAI

• Microsoft LLM Framework
• Microsoft Copilot
• Azure AI Search & Azure AI Services
• Microsoft Research
• Reference Architecture & Samples

Section 3 🌐: LLM Applications

• LLM Frameworks
• LLM Applications
  • Code Editor / Code Agent
  • Memory
• Caching, UX
• Proposals & Glossary: e.g., Vibe Coding, Context Engineering
• Robotics
• Awesome Demo

Section 4 🤖: Agent

• Agent Design Patterns
• Agent Frameworks
• Agent Applications
  • Code Interpreter
  • Model Context Protocol (MCP), A2A, Computer Use
  • Deep Research

Section 5 🏗️: Semantic Kernel | DSPy

• Semantic Kernel: Micro-orchestration
• DSPy: Optimizer frameworks

Section 6 🛠️: LangChain | LlamaIndex

• LangChain Features: Macro & Micro-orchestration
• LangChain Agent & Criticism
• LangChain vs Competitors
• LlamaIndex: Micro-orchestration & RAG

Section 7 🧠: Prompting | Finetuning

• Prompt Engineering
• Finetuning: PEFT (e.g., LoRA), RLHF, SFT
• Quantization & Optimization
• Other Techniques: e.g., MoE
• Visual Prompting

Section 8 🏄‍♂️: Challenges | Abilities

• AGI Discussion & Social Impact
• OpenAI Products & Roadmap
• Context Constraints: e.g., RoPE
• Trust & Safety
• LLM Abilities
  • Reasoning

Section 9 🌍: LLM Landscape

• LLM Taxonomy
• LLM Collection
• Domain-Specific: e.g., Software development
• Multimodal

Section 10 📚: Surveys | References

• LLM Surveys
• Business Use Cases
• Building LLMs: from scratch
• LLMs for Korean & Japanese
• Learning and Supplementary Materials

Section 11 🧰: AI Tools | Extensions

• AI Tools & Extensions

Section 12 📊: Datasets

• LLM Training Datasets

Section 13 📝: Evaluations

• LLM Evaluation
• LLMOps

Legend 🔑

• ref: external URL
• doc: archived doc
• cite: the source of comments
• cnt: number of citations
• git: GitHub link
• x-ref: Cross reference
• 📺: YouTube or video
• 💡 or 🏆: recommendation

Contributor 👀

ⓒ https://github.com/kimtth all rights reserved.

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Updated: 2025/07/02