This article does a good job separating model capability from system behavior. One point worth emphasizing is that most real failures come from the surrounding system, not the model itself. Tokenization choices, context truncation, retrieval quality, and update frequency often dominate output quality more than model size. In production, debugging usually means tracing data flow and state, not tuning prompts.