Context Engineering for Beginners: A Developer’s Guide

Context engineering is a crucial practice for AI engineers working with large language models (LLMs) in enterprise applications, aiming to address the limitations of prompt engineering by ensuring that models access the right data, structured correctly, at the right time. This discipline encompasses four core components: context definition, curation, integration, and governance. Context definition involves identifying the necessary data, user intent, business rules, and compliance constraints for a task, while curation focuses on organizing and cleaning information using tools like knowledge graphs. Integration connects curated context layers with the LLM system, facilitating the dynamic use of both structured and unstructured data, and governance ensures data access and visibility are controlled according to organizational policies. GraphRAG is highlighted as a practical technique that enhances context engineering by improving question answering through graph structures, making it particularly valuable for complex enterprise needs. Memgraph is presented as a high-performance graph database that supports context engineering by enabling efficient entity resolution and policy-aware context retrieval. Overall, context engineering is essential for building reliable, safe, and efficient AI systems across various enterprise functions.