What is Context Engineering?
Context Engineering goes beyond simple keyword matching, giving AI systems the ability to make logical decisions dynamically.
Beyond Naive Prompt Wrappers
When businesses try to implement AI, they often start with “naive RAG” (Retrieval-Augmented Generation). This involves dumping documents into a vector database and asking an LLM to search for matching keywords to answer a user’s prompt.
This approach fails when questions require synthesizing information across multiple, disconnected documents, like calculating total revenue across three different regional reports, or understanding how a feature launch impacts three separate product lines.
This is where Context Engineering becomes essential.
- Continuous Context Variables: The practice of not just retrieving data, but curating the exact state, history, and environmental variables the LLM needs to make a logical decision. It’s about feeding the model the ‘why’ along with the ‘what’.
By mastering this discipline and integrating advanced data structures like Graph RAG, you build decision engines that actually understand the nuanced context of your business, rather than just acting as glorified search engines.