This repository provides a comprehensive curriculum for learning Cloud Applied Generative AI Engineering (GenEng). It targets developers seeking to integrate generative AI technologies into applications, offering a structured path to proficiency with tools like OpenAI, Gemini, LangChain, and Pinecone. The benefit is equipping individuals with the skills to leverage AI for business transformation and economic growth.

How It Works

The course focuses on the "OPL stack" (OpenAI, Pinecone, Langchain) as a core toolkit for building generative AI applications. It emphasizes practical application development, distinguishing the role of developers from model creators. The curriculum covers integrating LLMs via APIs, managing conversational state with tools like the Assistants API, and utilizing vector databases for efficient data retrieval.

Quick Start & Requirements

• Installation: No specific installation commands are provided for the course materials themselves, but it heavily relies on cloud services.
• Prerequisites:
  • Microsoft Azure and Google Cloud accounts (free tiers available).
  • Azure OpenAI Service subscription.
  • Familiarity with Python, OpenAI API, LangChain, Pinecone, Streamlit, Containers, Serverless, Postgres, and Next.js is implied.
• Resources: Links to cloud provider free tiers and OpenAI API documentation are provided.

Highlighted Details

• Covers the OPL stack: OpenAI, Pinecone, and Langchain.
• Explores both OpenAI's Chat Completions API and the newer Assistants API.
• Includes practical project examples like building Q&A applications with LangChain and Pinecone.
• Discusses the economic impact and future trends of Generative AI.

Maintenance & Community

Information on maintainers, community channels (Discord/Slack), or a roadmap is not present in the README.

Licensing & Compatibility

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

Limitations & Caveats

The README is heavily focused on course content and industry context, lacking specific technical setup instructions or details on the project's own codebase. It assumes a significant level of prior knowledge in cloud platforms and AI development tools.