What is Context Engineering and Why Does Intelligent Data Connectivity Matter?

Context engineering is presented as the practice of supplying an LLM with the right minimum amount of information, tools, and data at the appropriate time, extending beyond prompt engineering to include system instructions, memory, external data, and tool access. As RAG and the Model Context Protocol make live enterprise data more accessible to LLMs, managing context has become important for controlling costs, improving reliability, and reducing hallucinations. CData positions its Connect AI platform as a context-engineering solution built on its SQL-based data connectivity foundation, offering access to more than 350 enterprise sources. Its approach aims to limit tool selection complexity by using eight tools regardless of connected systems, provide semantic understanding of standard and custom data objects, and process complex queries through source systems or the platform before returning results to the LLM. According to the text, these capabilities reduce token use and data-model exploration while leaving more of an LLM’s context window available for analysis and actions.

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