"Large language models are inherently context-blind. They do not understand your business, your customers, your policies, or the subtle decision logic that drives outcomes. When context is missing, they fill the gaps with generalized assumptions."
"Prompting is about interacting with AI. Architecting, or context engineering, shifts the focus toward building a system that continuously provides the right information, at the right time, in the right structure."
"A context graph captures the missing layer in enterprise systems. It connects entities such as customers, products, and services with relationships, decisions, rules, and outcomes, preserving decision traces behind actions taken."
Enterprises are rapidly adopting large language models (LLMs), but these models often fail to scale due to their context-blindness. They lack understanding of business specifics, customer needs, and decision-making processes. This leads to generalized assumptions and ineffective outputs. To address this, organizations should focus on context engineering, which involves designing systems that provide relevant information consistently. A context graph can capture the reasoning behind decisions, connecting various entities and preserving decision traces that are often overlooked in traditional systems.
#artificial-intelligence #large-language-models #context-engineering #enterprise-systems #decision-making
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