Context Caching
Context caching is an optimization technique where AI providers store and reuse previously processed prompt prefixes across multiple API calls. When you send the same system prompt, few-shot examples, or reference documents repeatedly, context caching avoids re-processing those tokens, reducing latency and cost. This is particularly valuable for applications that use long, static system prompts or large reference documents.
Example
You build a customer support bot with a 10,000-token system prompt containing product documentation. Without context caching, every customer query re-processes those 10,000 tokens. With caching, the system prompt is processed once and reused across all subsequent queries — cutting response time and API costs significantly.
Related Resources
Claude Opus 4.7 Prompting Guide: How to Get the Most From Anthropic's Top Model (2026)
A working reference for prompting Claude Opus 4.7 — extended thinking, 1M context, prompt caching, tool use, and the patterns that actually move quality and cost.
The Context Engineering Maturity Model: 5 Levels From Static Prompts to Orchestrated Systems
A 5-level maturity model for context engineering, from static hand-written prompts (L1) to multi-source orchestration with semantic caching and evaluation loops (L5). Self-assessment tool for teams.
Context Engineering: The 2026 Replacement for Prompt Engineering
How context engineering — the discipline of assembling what a model sees — replaced prompt engineering as the 2026 quality lever. Strategies, patterns, and trade-offs.
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