You are classifying token-management tactics by where each tactic applies in the request lifecycle: “Input...

Persist the validated output for downstream consumption and audit — Output Handling

Order prompt sections so cacheable content appears before per-request content — Prompt Construction

Summarize prior conversation history when full history is no longer needed — Input Preparation

Validate the model’s structured output against the expected schema — Output Handling

Move stable repeated content into a cacheable prefix at the beginning of the prompt — Prompt Construction

Trim retrieved passages to spans relevant to the user’s question — Input Preparation

Input preparation determines what evidence and history should enter the request, so summarization and retrieval trimming belong there. Prompt construction determines ordering and cache boundaries; stable repeated instructions must precede dynamic per-request content to maximize cache reuse. Output handling begins after generation and includes schema validation, persistence, auditing, and downstream delivery. Mixing these responsibilities creates inefficient prompts and weak validation boundaries. Anthropic’s context guidance emphasizes curating the smallest useful context because unnecessary tokens can reduce recall and accuracy. Prompt caching similarly depends on a stable reusable prefix, while structured outputs or schema validation protect downstream systems from malformed responses. Context windows ; prompt caching