Context Engineering: A Short Note

 
Context Engineering

Prompt engineering involves creating the best question or instruction to get the right response from AI. Meanwhile, context engineering is about giving the AI the right background information, or "context," so it can answer correctly and reliably.

As AI models become more advanced, the challenge isn't just about how to talk to them, but also about making sure they have the right information. Without context, AI can give "hallucinations" — answers that sound confident but are actually wrong. Context engineers create systems that collect conversation history, user profiles, important documents, and real-time data to provide the model, making chatbots more trustworthy or reliable.

Being skilled in context engineering leads to expertise in Retrieval-Augmented Generation (RAG), which connects AI to outside databases for the latest facts. A key part of context engineering is the Context Curation skill, which filters out unnecessary information to ensure only the important stuff gets to the model. In summary, context engineering transforms AI from a simple question-answering tool into a smart personalized assistant.

Context engineering is a related process that focuses on the context elements that come with user prompts, such as system instructions, retrieved knowledge, tool definitions, conversation summaries, and task metadata. This process is done to enhance reliability, provenance, and token efficiency in production LLM systems. Learning context engineering makes sure that changes to the provided context do not quietly change how the system behaves.