awesome-azure-openai-llm

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Basic Information

This repository is a curated collection and reference guide for Azure OpenAI, large language models (LLMs), and related applications. It aggregates resources across core topics such as Retrieval-Augmented Generation (RAG), Azure OpenAI platform details, LLM application development, autonomous agent design, prompt engineering and fine-tuning, model limitations and safety, and the broader LLM landscape. The README organizes content chronologically and provides concise summaries to help readers quickly understand each resource. It is intended to help practitioners, researchers, and developers find authoritative guides, architectures, tooling comparisons, and research surveys relevant to building, evaluating, and deploying LLM-based systems on Azure and with popular open-source frameworks. The repository is actively maintained and notes that the field evolves rapidly, so some entries may become outdated over time.

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Features
The README highlights several structured sections and navigational aids that define the repository’s features. It offers concise summaries for each listed resource and orders entries by date to surface recent developments. Core sections cover RAG system design, Azure OpenAI services and reference architectures, LLM applications and memory systems, agent development and the Model Context Protocol, prompt engineering and finetuning techniques, challenges and safety topics, and an overview of the LLM landscape. It also documents frameworks such as Semantic Kernel, DSPy, LangChain, and LlamaIndex, and includes practical tools, datasets, evaluation methods, and research surveys. A legend explains notation used across entries and contribution guidance is provided. The README emphasizes regular updates and active tracking of new materials.
Use Cases
The repository helps readers quickly discover curated learning and implementation resources for building LLM-based solutions and researching Azure OpenAI capabilities. It collects architecture references, design patterns, framework comparisons, and examples that assist with RAG implementations, vector database selection, and agent orchestration. Developers can find guidance on prompt engineering, finetuning strategies like PEFT and RLHF, and model optimization techniques including quantization. Researchers and practitioners benefit from survey papers, evaluation frameworks, datasets for training and benchmarking, and notes on LLM strengths and limitations. The sections on frameworks and tools point to practical libraries and orchestration approaches for application development. Overall, it reduces search time by centralizing up-to-date references and conceptual overviews for building and evaluating LLM applications.

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