While powerful proprietary Large Language Models (LLMs) were available, they often failed to adequately understand Southeast Asian languages, produced errors and hallucinations, and suffered from high latency.
This article is about that critical 3%, where we'll explore how to estimate performance impact, when to measure, what to look for, and practical techniques that work across different programming languages.
In this article, we will look at the high-level architecture of Zanzibar and understand the valuable lessons it provides for building large-scale systems, particularly around the challenges of distributed authorization.