I am a software engineer who has spent the past several years building production systems that use large language models. Not demos or prototypes. Systems that handle real traffic, real data, and real users who do not care how the magic works as long as it works reliably.

GenAI Patterns grew out of frustration. Every time I started a new LLM integration, I found myself solving the same structural problems from scratch. How should retrieval feed into generation? When does an agent need a planning step versus a simple loop? What happens when the model hallucinates inside a chain that other systems depend on? The answers existed, scattered across research papers, blog posts, framework documentation, and hard-won production experience. But nobody had organized them into a single, decision-oriented reference.

So I started writing one. What began as personal notes turned into a structured catalog. Each pattern follows the same format: the problem it solves, the core mechanism, when to use it, what can go wrong, and the honest trade-offs. The goal is not to be encyclopedic. The goal is to help you make better architectural decisions faster.