Time to First Token
Time to first token (TTFT) measures an early wait. It starts at a clearly named request point. It ends when the client or server sees the first generated token. Always state both points.
Another example
An engineer measures 1,000 streaming requests with equal prompt lengths, warm and cold groups, and server times. The report gives p50 and p95 TTFT for each group and names both measuring points. Total answer time and model prefill time without queueing are different measures.
Main parts
- 01declared start point
- 02first-token observation
- 03latency distribution
Use it when
Use this term when measuring how long streaming output feels like it takes to start under a repeatable setup.
Do not use it when
Do not compare TTFT when prompt size, batching, queueing, network boundary, cache state, or percentile rules differ.
Name used in code
t(first output token) − t(request start) Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate Time to First Token to make AI behavior measurable and tied to evidence. User context: An engineer measures 1,000 streaming requests with equal prompt lengths, warm and cold groups, and server times. The report gives p50 and p95 TTFT for each group and names both measuring points. Total answer time and model prefill time without queueing are different measures. AI method or concept: Time to First Token. Why it fits: Use this term when measuring how long streaming output feels like it takes to start under a repeatable setup. Do not use it when: Do not compare TTFT when prompt size, batching, queueing, network boundary, cache state, or percentile rules differ. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): It is official for that source. Other platforms or teams may use the term in another way. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of Time to First Token. Definition: Time to first token (TTFT) measures an early wait. It starts at a clearly named request point. It ends when the client or server sees the first generated token. Always state both points. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.