Context engineering vs prompt engineering: the difference

What this post added

This post clarifies the distinction between prompt engineering and context engineering, positioning context engineering as the broader discipline responsible for managing all information within an LLM's context window. It argues that production AI agent failures are often due to stale or fragmented context, which is an infrastructure problem, rather than prompt issues. The post introduces the concept of a 'context engine' as the infrastructure layer that assembles this context at runtime, highlighting Redis Iris as a solution that integrates retrieval, memory, and caching capabilities. It details how LangCache provides semantic caching to reduce LLM calls and latency, and how Agent Memory manages memory consolidation. The post emphasizes that context assembly at runtime, drawing from live data sources, is crucial for agent performance and addresses concurrency, consistency, and durability challenges.