This repository is a comprehensive educational resource focused on production-grade Generative AI systems, specifically designed for interview preparation and professional reference. Rather than being an executable application, it serves as a detailed knowledge base covering 16 critical areas of modern GenAI deployment, from foundational concepts like RAG systems and embedding technologies to advanced topics including multi-agent architectures, cost optimization, and system design. The content is organized into difficulty-levelled sections (Fresh, Intermediate, Advanced, Expert) with each topic featuring expected answers, common pitfalls (Red Flags), and real-world production statistics drawn from MENA enterprise deployments. What distinguishes this resource is its practical orientation - it emphasizes what actually works in production environments rather than theoretical concepts, with specific attention to trade-offs, failure modes, and optimization strategies observed in real implementations. While the repository contains no executable code and thus has no traditional software architecture or reusable components, it excels as structured educational content. The markdown-based organization allows for easy navigation, and the inclusion of SVG diagrams helps visualize complex architectures. The resource demonstrates particular strength in its coverage of production-specific concerns: evaluation methodologies, observability practices, security guardrails, and cost optimization techniques that are often overlooked in more theoretical treatments of GenAI topics.
This repository is a comprehensive collection of examples, tutorials, and applications demonstrating how to use LanceDB (a vector database) for various AI/ML applications. It showcases patterns for retrieval-augmented generation (RAG), multimodal search, chatbots, recommendation systems, and AI agents. The repository contains both Python and JavaScript/TypeScript implementations, with many examples featuring web interfaces using Streamlit, Gradio, or React. While it serves as a valuable resource for learning LanceDB integration patterns, the quality varies across examples, and some content is marked as archived.
This repository accompanies a Korean-language book on practical LLM application development, containing hands-on code examples organized by chapter. It demonstrates the full LLM lifecycle including foundational concepts (transformers), model optimization (quantization, PEFT), application building (RAG, agents, multimodal systems), and deployment techniques. The primary value lies in its educational notebooks that show how to implement LLM-powered solutions for tasks like Text2SQL, retrieval-augmented generation, and AI agents using frameworks such as LlamaIndex, AutoGen, and vLLM. While designed for Google Colab execution, the code illustrates reusable patterns for LLM engineering.