Context Caching
An optimization technique that caches computed attention states (key-value pairs) for repeated contexts – saves compute and reduces latency for similar queries.
Game changer for RAG and agent systems: Anthropic, OpenAI, Google offer native prompt caching. Reduces costs by 50-90% for recurring contexts.
Explanation
In transformer models, a key-value pair is computed for each token. With context caching, these are stored for system prompts, RAG documents, or frequent prefixes. Subsequent requests skip recalculation.
Marketing Relevance
Game changer for RAG and agent systems: Anthropic, OpenAI, Google offer native prompt caching. Reduces costs by 50-90% for recurring contexts. Critical for cost-effective enterprise AI.
Example
A RAG system with 50,000 token documentation: Without caching, every query pays for all tokens. With context caching, documentation is computed once – follow-up queries only cost new user questions. 80% cost reduction.
Common Pitfalls
Cache invalidation on context changes. Not all providers support it. Memory overhead for cache storage. TTL management needed. Only works with exactly matching prefix.
Origin & History
Context Caching is an established concept in the field of Technology. The concept has evolved alongside the growing importance of AI and data-driven methods.