Prompt Caching & MCP Protocol: Production AI Optimization

The part of prompt caching nobody optimises for (until it bites)

The standard pitch is caching = save 80-90% on tokens. True. What gets left out of most write-ups, and what engineers on r/LangChain and r/OpenAI keep learning the hard way, is that caching is only a win when your cache hits. And whether it hits depends on architectural choices that look innocent until you measure them.

Three concrete traps worth naming:

• →TTL asymmetry is real. Anthropic's default TTL is 5 minutes, OpenAI's 1 hour, Google's in between. If your traffic is bursty with 10+ minute quiet periods, Anthropic's cache will evaporate between bursts and your "savings" will quietly vanish. Anthropic's prompt caching docs now offer 1-hour cache tiers at a premium — worth the math for quiet-but-steady workloads.
• →Cache boundaries must match change frequency. If you shuffle RAG chunks into a different order between requests, the cache breaks. Sort retrieved chunks by a stable key (document ID) before concatenating; this single change has single-handedly saved teams five-figure monthly bills.
• →Dynamic system prompts are a silent cache killer. Injecting the current timestamp or a user ID into the system prompt seems harmless. It invalidates the cache on every request. Move anything dynamic to the end of your prompt, always.

On the MCP side, the official Model Context Protocol spec is short and readable; if you're still writing bespoke function schemas per vendor in 2026, you're building tech debt. The MCP announcement from Anthropic is worth five minutes to understand why this standard won the race.

Prompt Caching: Stop Paying Twice for the Same Tokens

Every API call sends your system prompt + RAG context + conversation history. If your system prompt is 2,000 tokens and stays the same across all queries, you are paying for those 2,000 tokens every single time. Prompt caching tells the API: "I already sent this prefix, just reuse it."

Request 1: [8000 static tokens] + [2000 dynamic tokens] = 10,000 tokens billed
Request 2: [8000 static tokens] + [2000 dynamic tokens] = 10,000 tokens billed
Request 3: [8000 static tokens] + [2000 dynamic tokens] = 10,000 tokens billed

Total: 30,000 tokens billed for 6,000 tokens of unique content

MCP: The Model Context Protocol

MCP Server: 'Weather Service'
Tools:
  - get_weather(city, units)
  - get_forecast(city, days)
Resources:
  - weather://cities -> list of supported cities
  - weather://alerts -> active weather alerts

Production Optimization Checklist

Test Your Understanding

Congratulations!

You have completed Module 9 and the entire advanced AI curriculum. You now understand:

• →Context engineering, designing the information environment for AI
• →Lost-in-the-middle, position effects and optimization
• →Production optimization, caching, MCP, and cost management

These are the skills that separate prompt hobbyists from production AI engineers.

Return to the Module 9 overview to review your progress and explore next steps.