Have you ever noticed that your GenAI applications are spending massive amounts of time and money re-reading the exact same setup text?
Every time a user asks a short question in a chatbot, the Large Language Model (LLM) must re-read your entire 2,000-word corporate playbook, your agent's system rules, and the full chat history from scratch.
This phase is called the pre-fill math phase, and it drives up both your cloud bill and your user latency (Time-to-First-Token).With Amazon Bedrock Prompt Caching for Claude 4.6 (both Sonnet 4.6 and Opus 4.6), this problem is completely solved. You can achieve up to a 90% cost reduction on input tokens and an 85% drop in latency by using a clever architectural shortcut.
Here is exactly how it works under the hood, how AWS maintains it across API requests, and how to implement it using Python.
The Secret Architecture: Model Inference vs. AWS Infrastructure
