This repository serves as a code companion for a Udemy Prompt Engineering course, offering practical Jupyter notebooks and example applications for AI development. It targets course students and developers seeking hands-on experience with prompt engineering, OpenAI APIs, Retrieval Augmented Generation (RAG), agents, LangChain, and image generation, providing a valuable resource for building and understanding LLM applications.

How It Works

The project is structured around Jupyter notebooks, small example applications, prompt files, datasets, screenshots, and diagrams, eschewing a single installable Python package. This approach facilitates direct engagement with code, enabling users to explore and implement core concepts like prompt optimization, RAG pipelines, agent orchestration, and multimodal AI workflows through practical, runnable examples.

Quick Start & Requirements

• Primary Install/Run: Launch JupyterLab locally via python -m venv .venv, source .venv/bin/activate, pip install jupyterlab, and jupyter lab. Many notebooks install additional dependencies inline using %pip install.
• Prerequisites: OPENAI_API_KEY is frequently required. Additional service credentials may be necessary for specific notebooks, including ANTHROPIC_API_KEY, SUPABASE_URL/SUPABASE_KEY (for pgvector), LANGCHAIN_API_KEY, TAVILY_API_KEY, FAL_KEY, Twilio/ngrok (for realtime voice), and Hugging Face/GCP credentials (for older image models).
• Links: Compatibility with Google Colab is noted.

Highlighted Details

• Features 248 notebooks covering prompt engineering, OpenAI API workflows, RAG, agents, LangChain, LangGraph, evaluation, vision, and image generation.
• Explores advanced prompting techniques (Few-Shot, Chain of Thought, Self-Consistency), RAG with vector databases, agent design patterns (OpenAI Agents SDK), and LangGraph state management.
• Includes dedicated sections for vision and image models, covering multimodal analysis and image generation workflows (FLUX/FAL).
• Provides supporting assets such as sample datasets, prompt files, screenshots, and architecture diagrams.

Maintenance & Community

No specific details regarding contributors, community channels (e.g., Discord/Slack), or a public roadmap are provided in the README.

Licensing & Compatibility

The repository's license is not specified in the README.

Limitations & Caveats

This repository is a code companion and does not mirror every lecture from the Udemy course, which includes non-coding conceptual material. Dependencies are often managed inline per notebook, lacking a unified requirements.txt or pyproject.toml. Some sections, like standard_image_model_practices/, are placeholders, and several vision/image notebooks may require external assets or cloud credentials to run fully.