What are the best practices for setting up semantic caching to cut down our monthly API token costs by half
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What are the best practices for setting up semantic caching to cut down our monthly API token costs by half
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.
ChatGPT real searches (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
semantic caching best practices LLM applications cache hit rate similarity threshold multi tenant invalidation Redis documentation Google ↗
OpenAI API prompt caching documentation cached input tokens semantic caching distinction Google ↗
- Best practices · docs.aws.amazon.com
- Redis semantic cache · redis.io
- pgvector documentation · github.com
- GPTCache GitHub repository · github.com
- Prompt caching · developers.openai.com
- Prompt caching · developers.openai.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
semantic caching best practices LLM applications cache hit rate similarity threshold multi tenant invalidation Redis documentation OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 | |
OpenAI API prompt caching documentation cached input tokens semantic caching distinction OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |