How do you optimize prompt performance and reduce costs?
Start by cutting unnecessary context and enforcing strict output formats to reduce retries. Cache frequent results. Choose the smallest model that meets quality for each subtask. Batch operations where possible, and prefer staged flows that fail fast. Monitor token counts, latency, and success rates; most savings come from fewer corrections and re-runs, not shaving a sentence here or there.
In practice
Track ‘attempts per successful result.’ If it’s >1.2, fix prompting or validation before hunting token pennies.
Where to start
- [01]Instrument prompts for tokens, latency, and success.
- [02]Remove boilerplate context; keep only task-relevant data.
- [03]Introduce response validation to avoid needless retries.
- [04]Split complex flows into smaller, cheaper steps.
prompt optimizationAI cost reductionefficient promptingtoken savings