Caching model understanding may cut latency and token cost
When users wait 5 to 20 seconds for an answer, common fixes are caching, , and better . But often saves the source chunks that were found, not the meaning already drawn from them. The model still has to read those chunks again, process them again, and pay the again.
A stronger option is to cache the understanding created from the chunks instead of caching only the chunks. This could reduce work, context building, token use, and total response time. The hard part is freshness.
When source documents, APIs, code, or change, the saved understanding must be in at the right time.
Key points
- Caching can reduce repeated work, but it may not reduce token use enough.
- often still makes the model read saved chunks again.
- Caching the model’s understanding could reduce both delay and cost.
- This approach may reduce , context building, and token use.
- In is the main challenge when source data changes.