AI System Design Guide is a reference resource that covers production AI systems, RAG architectures, LLM engineering, and agentic AI for engineers building and interviewing for roles in AI.
The guide addresses the need for practical, production-focused knowledge in AI system design by providing structured chapters on model selection, evaluation frameworks, RAG fundamentals, and real-world case studies drawn from staff-level interviews. It combines interview preparation material with engineering patterns for building AI systems at scale, covering topics including retrieval-augmented generation, LLM selection, agentic workflows, and evaluation methodologies.
Engineers preparing for AI system design interviews or building production AI systems should use this guide. It suits teams evaluating LLM architectures, designing RAG pipelines, or implementing agentic systems. The guide includes a question bank for interview preparation alongside practical chapters on model taxonomy and RAG fundamentals, making it useful both for candidates and for engineers designing systems in production environments.
The project maintains active development with continuous updates to chapters and interview content. Pull requests are welcomed, indicating openness to community contributions. The guide is presented as a living reference rather than a static document, with material refreshed to reflect changes in the AI landscape.
