AI Optimization
Token Utilization
Optimize every token without sacrificing quality.
Measure input, output and cache behavior, then remove repeated context and inefficient agent loops.
Token utilization measures whether input, output and cached tokens contribute to a successful result. Optimization focuses on unnecessary context, repeated instructions, low cache reuse, unsuitable models and workflows that loop without producing a better outcome.
Find the major waste sources
The largest savings usually come from architecture and workflow changes rather than micro-editing every prompt.
- Repeated repository context on every request
- Oversized tool results and logs
- Unbounded agent retries
- Expensive models used for routine transformations
- Long conversations that should be summarized or reset
Optimization sequence
Change one lever at a time and compare both cost and result quality against a representative task set.
- Establish a baseline by workflow
- Trim and retrieve context selectively
- Introduce prompt caching where repetition is stable
- Route simple work to cheaper capable models
- Set retry, tool-output and context limits
FAQ
What is a good token utilization rate?
There is no universal percentage. Define success by workflow, then compare tokens and cost per successful result over time.
Do fewer tokens always mean lower cost?
Usually, but model choice, cache pricing, long-context tiers and retries can change the relationship. Measure billed cost as well as raw tokens.