4 posts
LLM monitoring is the discipline that makes a production language-model system's quality, cost, and performance visible through logging, tracing, and alerts. What to log and how to measure it.
What is LLM latency? Time to first token (TTFT), per-token time, streaming, and user perception: the components that determine response time and acceptable thresholds.
Voice AI agents are far more than simple pipelines that convert speech to text and text back to speech. Real enterprise value emerges from the system’s ability to understand spoken input, manage natural dialogue flow, know when to speak and when to stay silent, and maintain responsiveness without interrupting users or creating awkward delays. A strong voice agent architecture therefore depends on the joint design of STT accuracy, TTS naturalness, turn-taking quality, barge-in handling, streaming infrastructure, latency budgets, context management, and safe action execution. This guide explains how to build production-grade Voice AI agents through the lenses of STT, TTS, conversational timing, latency design, architecture choices, evaluation metrics, enterprise use cases, and common design mistakes.
When enterprises select a large language model, they often focus too heavily on benchmark scores, popularity, or the idea of using the “most powerful model.” In production, however, the real decision depends on much more: how usable the context window actually is, time to first token, end-to-end latency, throughput capacity, cost per request and per token, human correction effort, and the level of quality required by the use case. A larger context window does not automatically mean a better user experience, lower latency does not always create more business value, and a cheaper model may still result in a higher total cost of ownership. This guide explains how enterprises should think about the trade-offs between context window, latency, cost, and quality when choosing LLMs for real production environments.