Presentation: Beyond Prompting: Context Engineering for Production-Grade AI
Moving beyond prompt engineering: practical architectural patterns for production AI that control costs and latency.
Why it matters
A practitioner's guide to the engineering scaffolding required to ship AI applications at scale — memory management, token optimization, and caching strategies that separate proof-of-concept from production.
The key facts
9 to knowLong-term and short-term memory integration using Redis
Token limit management via summarization
Context rot mitigation with reranking and semantic caching
API cost control under strict latency constraints
Production-grade architectural patterns beyond prompt engineering
Long-term and short-term memory integration patterns using Redis
Token limit management via summarization strategies
Context rot mitigation using reranking and semantic caching
Focus on production-grade architecture, not prompt optimization
Go to the source
InfoQ AI/MLinfoq.com
Publisher excerpt: Ricardo Ferreira discusses moving beyond simple prompt engineering to build production-grade AI applications. He shares practical architectural strategies for integrating long-term and short-term memory using Redis, managing LLM token limits via summarization, mitigating context rot with reranking…