Prompt Caching: What Most Builders Ignore (Video)
Source: YouTube
Ingested: 2026-08-30
Creator: nate-b-jones
Executive Summary
In this tactical broadcast, nate-b-jones delivers a direct engineering mandate to AI API builders and agent developers: take prompt-caching seriously and audit your plugin architectures. Highlighting that prompt caching is foundational table-stakes in 2026 rather than an advanced feature, Nate demonstrates how failing to cache stable prefixes needlessly inflates API bills and degrades operational efficiency.
Key Tactical Takeaways
1. The 90% Cost Reduction Margin
- Repeated context without caching results in severe financial inefficiency.
- Reusing cached input tokens yields up to a 90% discount on model inputs.
- On frontier models like Claude Opus (anthropic), cache hits cost 5.00 per million tokens standard input pricing.
2. Mandatory Plugin & Context Audits
- Developers must audit their plugins, harnesses, and system wrappers to ensure static context is structured properly for caching.
- Failure to separate static instruction prefixes from dynamic run-time user inputs breaks cache hits and wastes tokens.
3. The Three Layers Every Builder Must Cache
API builders and harness engineers must ensure the following components are cached:
- System Prompts: Core agent behavioral instructions and rules.
- Tool Definitions: Schemas, function specifications, and MCP tool descriptions (see harness-design).
- Reference Documents: Static context bases, domain files, and background manuals.