A deep dive into what it really takes to build dependable software on top of inherently unpredictable LLMs. Covers architectural patterns, failure modes, evaluation pitfalls, hallucination-resistant design, guardrails, system decomposition, and how to treat LLMs as probabilistic components rather than “smart APIs.” Focused on engineering practices rather than hype, with practical frameworks for building stable, observable, testable LLM-based systems.