LEARNER · GLOBAL
What is prompt caching and how it cuts LLM inference costs in BFSI workflows
Prompt caching: Storing the 'system instructions' and common reference data (e.g., regulatory rules, product docs) in a cache so the LLM doesn't re-process them on every query. Think of it like a sticky note you keep in front of the AI—it remembers context without burning through expensive tokens. Cuts inference cost by 50–90% when queries repeat on cached context.
WHY IT MATTERS
Direct cost lever for BFSI deploying LLMs at scale (customer service, compliance Q&A, trade review). Even modest latency improvements (via caching) translate to lower per-query cost, enabling wider adoption. Compliance teams should audit cached rules for staleness.
Source: Anthropic · 2026-05-21