Context Engineering Visual Analogy

Think of an LLM as a ginormous word cloud that navigates using probability. At the centre sits a dense core of the most common, generic language. When you ask a vague question, the model starts here and you get a vague answer. Effective prompt engineering is about navigating away from that dense centre toward the region of the word cloud most relevant to your actual need. A well-crafted prompt acts like coordinates, pointing the model toward more specific, useful territory.

But despite the hype around prompt engineering, we've now progressed to the next layer. It's no longer just about finding the best-suited words for your problem. It's about ensuring the model's starting point for exploration is already positioned around your evolving needs, so that every answer is rooted in your context from the outset.

Think of your context as a magnetic anchor within that word cloud. When context is rich and well-structured, the anchor holds the model in your territory. But context has a finite window as new information enters the conversation, older context gets pushed out, and that anchor drifts back toward the generic centre. So the real question isn't just "how do I write a better prompt?" It's: what configuration of context is most likely to generate our model's desired behaviour?