Prompt Caching
AssessTechniques
A method for reusing cached prompt context to reduce repeated model inference costs.
Why it's here
Placed in Assess: 2 article(s) of evidence from 2 source(s), led by research-stage coverage, with 2 in the last 30 days. Confidence 38%.
Evidence (2)
- 6Hacker News·7/31/2026researchWhy AI session portability is fading
The article argues that inference APIs are increasingly returning provider-bound state instead of a fully portable transcript. It says features like encrypted reasoning tokens, hidden subagent messages, server-side response IDs, and non-portable search or cache references make AI sessions harder to inspect, export, replay, audit, or delete independently of the provider.
- 6The New Stack·7/23/2026researchPrompt Caching and Batched Pipelines to Cut RAG Costs
The article argues that naive retrieval-augmented generation (RAG) architectures often fail in production because synchronous ingestion, timeout limits, and rate limits do not scale to enterprise workloads. It recommends asynchronous uploads, micro-batching, and controlled embedding workers, and discusses prompt caching as one way to reduce costs without hurting accuracy.