What strategies work best for optimizing token consumption in long-context RAG applications without sacrificing retrieval accuracy?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What strategies work best for optimizing token consumption in long-context RAG applications without sacrificing retrieval accuracy?
1 real search · 1 real-search pass
long context retrieval augmented generation token efficiency contextual compression reranking hybrid retrieval research 2025 2026Google ↗
OBSERVEDReason
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
long context retrieval augmented generation token efficiency contextual compression reranking hybrid retrieval research 2025 2026 Google ↗