Awesome Azure Openai Llm Overview

A curated list of 🌌 Azure OpenAI, 🦙 Large Language Models (incl. RAG, Agent), and references with memos.

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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.
🔹The dates are based on the first commit, article publication, or paper version 1 issuance.
🔹Capturing a chronicle and key terms of that rapidly advancing field.
🔹Disclaimer: Please be aware that some content may be outdated.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 | Application
  • GraphRAG
• Vector DB

Section 2 🌌: Azure OpenAI

• Microsoft LLM Framework
• Copilot Products & Azure OpenAI Service | Research
• Azure Reference Architecture

Section 3 🌐: LLM Applications

• LLM Frameworks | Applications
  • Code Editor
  • Memory
• Caching, UX, Proposals & Other Topics
• LLMs for Robotics
• Awesome Demo

Section 4 🤖: Agent

• Agent Design Patterns
• Agent Frameworks | Applications
  • Code Interpreter
  • Model Context Protocol (MCP) & 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 LLMs: e.g., Software development
• Multimodal LLMs

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

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