I’m the author. I wrote this piece because a lot of discussions around LLM-based systems focus on prompts or model benchmarks, while the real complexity starts at the system level: how components interact, how failures propagate, how to evaluate behavior over time, and how to keep nondeterministic elements inside a predictable architecture.

The article tries to map this landscape and highlight patterns that seem essential when building anything more serious than a single-model call: • where unpredictability actually comes from • how architecture shapes reliability • why evaluation is harder than it looks • and what guardrails or control layers help keep the system stable

If anyone is working on similar problems or wants to challenge any of the points — happy to discuss, compare approaches, or clarify specific sections.

A deep engineering guide to building reliable LLM-based systems. Covers failure modes, hallucination control, evaluation traps, system decomposition, guardrails, and architecture patterns for treating LLMs as probabilistic components rather than deterministic logic. Focused on real engineering challenges, not hype.