Context Engineering Is Changing. Here’s What It Means for Data Scientists

Context engineering is replacing prompt engineering as the key method for interacting with agentic AI systems like Anthropic's Claude. This approach involves providing a collection of documents and instructions, known as context, for the model to use when performing tasks. For data scientists, this means trusting the model with general directions instead of overly specific, potentially contradictory instructions. It also involves modularizing tasks into a tree of specific "skills" and leveraging the model's ability to remember user habits and update its own context files. Ultimately, providing well-structured and rich context through various file formats is crucial for maximizing the performance of modern large language models.