Hands-On AI Engineering is a curated collection of practical AI projects that demonstrates implementation patterns for OCR systems, RAG pipelines, AI agents, and other production-oriented AI use cases.
The repository addresses the challenge of learning to build real-world AI applications by providing complete, working implementations across multiple modalities. Rather than abstract tutorials, each project includes full source code, setup instructions, and documentation designed to be adapted for production use. The projects span language models, multimodal systems, OCR, retrieval-augmented generation, and multi-agent workflows, with implementations using various model providers including OpenAI, Anthropic, Google, and open-source alternatives.
This collection suits developers who learn best through concrete examples and want to understand how to integrate different AI capabilities into working systems. It is particularly valuable for those building RAG systems or agent-based applications, as these represent the primary focus areas. The repository emphasizes production-readiness and best practices rather than toy examples, making it suitable for developers preparing to deploy AI features in real applications.
The project receives regular updates with new implementations added to expand the collection of use cases. The repository maintains active community engagement through contribution guidelines and demonstrates sustained development activity focused on expanding the range of practical examples available.
