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'Context engineering turns context into a core design layer for AI agents, focusing on JIT retrieval, memory strategies, and tool design to keep reasoning precise and efficient.'
'ACE' upgrades LLMs by incrementally editing a persistent context playbook rather than fine-tuning; it yields notable accuracy gains and 80–90% reductions in adaptation latency on agent and finance benchmarks.
'Real-world case studies show context engineering driving error reduction, productivity gains, cost savings, and better user experiences by grounding LLMs with dynamic, multi-source data.'
Discover how context engineering advances large language models beyond prompt engineering with innovative techniques, system architectures, and future research directions.
Discover key lessons from the Manus project on context engineering that improve AI agents' performance by optimizing how they process and manage decision-making information.
Context engineering enhances AI performance by optimizing the input data fed to large language models, enabling more accurate and context-aware outputs across various applications.