What is Context Engineering?

Context engineering is a critical practice in designing the information inputs for large language models to ensure they produce reliable and accurate outputs, acting as an intermediary between data platforms and model prompts. This engineering discipline is increasingly vital as AI expands beyond traditional roles into areas like customer support and sales, where models rely on external, often untrained data. The core challenge lies in maintaining data freshness, fragment completeness, governance, and schema stability to prevent models from generating outdated or incorrect responses. Effective context engineering enables models to function as fluent analysts, allowing them to integrate diverse data sources and provide accurate, contextually rich answers. By treating context as a foundational platform rather than an isolated feature, organizations can enhance AI applications across various domains, leveraging tools like Starburst's Icehouse architecture for a unified, governed query experience that supports both analytics and AI workloads.