TTFT (Time-to-First-Token) is a performance metric used in AI systems, especially large language models (LLMs), to measure how quickly the model begins generating a response after receiving a prompt. It refers to the time taken between when a user submits a request and when the first token (word or partial word) of the model’s response is produced. This is similar to the “loading” moment after you ask a question and just before the model starts typing an answer.

To dive deeper into the performance metrics that impact AI agent responsiveness, explore the AI Agents for Everyone Specialization* on Coursera. It offers hands-on guidance to help you build and optimize agents that deliver results in real-time environments.

Think of TTFT as the digital version of someone taking a breath before they begin speaking. A shorter TTFT makes the AI feel faster and more responsive, which is especially important in real-time applications like customer service chats, virtual assistants, and search experiences. Even if the rest of the response takes a bit longer to complete, users tend to feel more satisfied when there’s less delay before the AI starts replying.

Behind the scenes, TTFT is influenced by several factors: the complexity of the prompt, the model’s size, system latency, and how efficiently the backend infrastructure processes and routes data. For example, smaller models or models running on high-performance hardware may achieve faster TTFTs compared to massive models operating in a cloud environment with lots of traffic.

Developers often optimize TTFT by using techniques like model quantization (to reduce size), server caching, or even fine-tuning models to respond more efficiently to certain prompt types. While total generation time is important, TTFT specifically helps gauge how quickly users see feedback, which can be critical for creating smooth and natural AI interactions.