what strategies actually work for managing llm token budgets when you have hundreds of daily active users querying long documents
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
what strategies actually work for managing llm token budgets when you have hundreds of daily active users querying long documents
1 real search · 1 real-search pass
production RAG long documents token budget retrieval reranking context engineering prompt caching official documentation OpenAI AnthropicGoogle ↗
OBSERVED
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 1 queries…
Searches and sources
ChatGPT1 real searches · gpt-6
production RAG long documents token budget retrieval reranking context engineering prompt caching official documentation OpenAI Anthropic Google ↗
- Contextual retrieval in AI systems \ Anthropic · anthropic.com
- architecture-center/docs/ai-ml/guide/rag/rag-information-retrieval.md at main · MicrosoftDocs/architecture-center · github.com
- Prompt caching · developers.openai.com
- Prompt caching · developers.openai.com