Prompts contain long context that is rarely used. What is the first step?
Why C is the answer
Estimate tokens and trim the context
Estimating tokens shows where context is wasted, so you can trim what is rarely used.
In detail
- Estimate tokens first, then trim the context.
- Measure before and after.
Use case
An illustrative scenario, not a real incident.
A team counts tokens per request and finds that half is unused context, so it trims that first.
See it in different ways
flowchart TD
A["Request"] --> B{"Answer in the cache?"}
B -->|Yes| C["Return it, no model call"]
B -->|No| D{"Simple?"}
D -->|Yes| E["Lower-cost model"]
D -->|No| F["More capable model"]mindmap
root((Cost))
Avoid calls
Semantic caching
Smaller prompts
Trim context
Prompt caching
Right model
Tiered usageflowchart LR S0["Estimate tokens"] --> S1["Find unused context"] --> S2["Trim"] --> S3["Check quality"]
Key point: Estimate tokens, then trim.
Why the other options are wrong
- A. Add a second knowledge base
- That adds context.
- B. Switch the application to another Region
- Region does not trim prompts.
- D. Move to a larger model
- A larger model costs more.