How can I optimize our prompt engineering templates to reduce redundant API calls and stay within strict internal budgeting limits when managing LLM token budgets?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How can I optimize our prompt engineering templates to reduce redundant API calls and stay within strict internal budgeting limits when managing LLM token budgets?
1 real search · 1 real-search pass
LLM prompt caching token optimization reduce API calls prompt engineering templates official documentation OpenAI AnthropicGoogle ↗
OBSERVEDOther / navigational
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
LLM prompt caching token optimization reduce API calls prompt engineering templates official documentation OpenAI Anthropic Google ↗
- Prompt caching · developers.openai.com
- Understanding and counting tokens · help.openai.com