auditing-prompt-token-budget

You reduce Anthropic API token costs by auditing prompt structure to separate stable text from volatile data, then placing cache_control breakpoints to cache the stable prefix and reduce per-call token billing.

Prompt caching stores stable prompt prefixes to lower read costs, and TTL selection impacts API costs by matching 5m for short in-conversation reuse or 1h for cross-call reuse across hours or days.

To audit a prompt for context window pressure, perform exact token counting across system text, tool definitions, messages, and tool results to pinpoint segments approaching the context limit or inflating per-call cost.

Place cache_control breakpoints in a RAG workflow on the last block of the stable region to cache the correct prefix, ensuring volatile retrieved documents remain outside the cached boundary to maintain hit rate.

Low cache hit rates in Anthropic API prompts are caused by bad patterns including duplicated boilerplate, volatile data placed in cached blocks, or defining cache segments that are too small to offset the write cost overhead.

Yes, this token budgeting approach works for few-shot systems near the context window by applying exact token counting and cache efficiency telemetry to isolate stable few-shot examples and reduce overall API token pressure.