Does your AI agent really understand your enterprise context? You need more than a knowledge graph, says Google Cloud's Andi Gutmans
Context is at the center of the conversation about enterprise AI these days. It's become increasingly clear that it plays a huge role in reducing token spend and increasing the accuracy of responses from Large Language Models (LLMs). Give the model sufficient context, and it is able to narrow its search before it gets started and validate its responses. Without that, it may churn through many more cycles of inference than it needs to, and still come back with a response that's incomplete, inaccurate, misleading, or worse.
What do we mean by context? It includes a range of items, such as the established processes and policies of the organization, the specialist vocabulary and customs of its industry and functional teams, the role and access rights of the individual or agent making the request, their previous interactions and other aspects of the history leading up to the request, the transactional data...
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