Are there any quick fixes to reduce API latency when making bulk requests to our language model provider during our morning data processing batch?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Are there any quick fixes to reduce API latency when making bulk requests to our language model provider during our morning data processing batch?
1 real search · 1 real-search pass
language model API latency optimization batching concurrency connection pooling rate limits prompt caching official documentationGoogle ↗
OBSERVEDInstruction
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
language model API latency optimization batching concurrency connection pooling rate limits prompt caching official documentation Google ↗
- Prompt caching · developers.openai.com
- Prompt caching · developers.openai.com
- Rate limits · developers.openai.com
- Production best practices · developers.openai.com
- Troubleshooting API rate limits and 429 errors · help.openai.com