DjangoPeng/openai-quickstart resource

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1773 stars · 1163 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A comprehensive open-source tutorial and learning guide for developing applications with large language models, built as Jupyter Notebooks. It covers LLM theory (BERT/GPT), OpenAI API development, and hands-on GenAI application building with LangChain.

Use cases

• learn how large language models like GPT and BERT work
• get started with the OpenAI API and function calling
• build a RAG chatbot with LangChain
• learn LangChain for GenAI application development
• find examples of AutoGPT and machine translation with LLMs
• understand the LLM ecosystem including Hugging Face and ChatGLM
• learn about data privacy and legal compliance for LLM apps

When to choose

• you want a structured, hands-on tutorial with runnable Jupyter notebooks
• you are new to LLM development and need theory plus practice in one place
• you want to learn LangChain through concrete examples like RAG chatbots

When to avoid

• you need a production-ready library or framework to ship in your app
• you want a maintained software tool rather than educational material
• you need coverage of non-OpenAI providers beyond what the notebooks include

Facets

learning-resource · maturity active

machine-learning llm-inference rag agent-framework prompt-engineering chatbot nlp large-language-models artificial-intelligence tutorials python cross-platform cli langchain openai jupyter-notebooks genai gpt-4 chatglm hugging-face hands-on-examples course-material retrieval-augmented-generation ai-agents natural-language-processing

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