How can I optimize our prompt routing to cut API costs and reduce response latency simultaneously
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How can I optimize our prompt routing to cut API costs and reduce response latency simultaneously
2 real searches · 1 real-search pass
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.
Classifying 2 queries…
Searches and sources
ChatGPT2 real searches · gpt-6
OpenAI API prompt caching latency cost optimization model routing official docs Google ↗
LLM routing cost latency quality cascade routing research production best practices Google ↗
- Prompt caching · developers.openai.com
- API deployment checklist · developers.openai.com
- Prompt caching · developers.openai.com
- Latency optimization · developers.openai.com
- API deployment checklist · developers.openai.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
OpenAI API prompt caching latency cost optimization model routing official docs OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
LLM routing cost latency quality cascade routing research production best practices OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |