Beyond Prompting: Context Engineering and Memory Management Scale AI Systems

A new InfoQ presentation on context engineering and memory management techniques for AI systems at scale shows how enterprises can optimize LLM inference beyond simple prompt tuning. Topics include token budgeting, context window prioritization, and multi-turn conversation state management.

For platform teams scaling LLM applications, these patterns address latency and cost leakage in production deployments. The presentation highlights that naive prompt expansion and context caching require architectural rethinking to avoid runaway token costs in high-volume customer-facing systems.