Beyond Prompts: The Case for Context Engineering in Enterprise AI
Strong prompts help with AI performance, but real enterprise value comes from context, data discipline, and sound technical architecture.
Enterprises have begun to discover what the generative AI hype can obscure: large language models are convincing but inconsistent unless fed the right data.
Markets move on data and analysis; a misplaced figure, a stale disclosure, or a hallucinated data point can make the difference between sound judgment and costly error.
The true differentiator in enterprise-grade generative AI isn’t style, but substance – specifically, context engineering: the structuring, selection, and delivery of the right data into an AI system’s context window at the right moment.
Without it, models are more likely to hallucinate, miss critical signals, or provide generic answers unfit for high-stakes decision-making.
Author's summary: Context engineering is key to enterprise AI success.
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