A systematic, open-source Python tutorial covering the full LLM application development stack from basics to enterprise-level Agent and RAG projects. It targets developers who want runnable code examples, practical enterprise use cases, and an interview question bank without relying on paid bootcamps.
Project overview
It combines a structured curriculum with runnable source code, enterprise-grade practical projects like NL2SQL and multi-agent research systems, and an interview question bank in a single open-source repository.
Project type
AI Agent · RAG · Prompt Engineering · Learning Resources
Use cases
Knowledge Q&A · Search & Research · Data Analysis · Learning & Education
Deployment
Refer to project documentation
License
MIT
Best for
Developers seeking an open-source, structured Python curriculum for building LLM applications, RAG systems, and agents.
Learners who want runnable code examples and enterprise-level practical projects like NL2SQL and multi-agent deep research systems.
Job seekers preparing for AI application development roles using an aligned interview question bank.
Key capabilities
Covers a progression from LLM basics and prompt engineering to low-code platforms, core frameworks, enterprise RAG and Agent projects, and fine-tuning.
Delivers runnable code examples for AI concepts and frameworks, accompanied by environment setup instructions and troubleshooting guidance.
Includes an interview question bank aligned with standard AI application developer job descriptions and training program competencies.
Provides practical projects such as NL2SQL e-commerce Q&A and multi-agent deep research systems to demonstrate enterprise application deployment.
Limitations and risks
The tutorial focuses exclusively on the Python ecosystem and excludes Java frameworks like Spring AI.
Getting started
Clone the repository and set up a Python virtual environment with the necessary dependencies installed.
Configure your LLM API keys, such as Qwen or DeepSeek, in the .env file. Optionally, configure local models via Ollama.
Run the first LangChain HelloWorld Python script to verify your environment and setup.
Alternatives and comparisons
Provides self-contained Python code samples covering LLM patterns, tool-use, and agents using frameworks like LangChain and LlamaIndex.
Provides a theory-and-practice guide to build AI Native Agent systems, including hands-on implementation of ReAct, Plan-and-Solve, and multi-agent projects.