When we interact with an AI, we use a prompt. It’s the question we ask or the command we give. You might ask a chatbot, “What are the main themes in the novel Dune?” or tell an image generator, “Create a picture of a friendly robot reading a book in a sunlit library.”

Crafting a good prompt is a valuable skill, often called prompt engineering. It's about phrasing your request in a way that gets the AI to produce the best possible result. But a well-written prompt is only half the story. For an AI to be truly helpful, especially in complex situations, it needs more than just a good question. It needs context.

Think of it like talking to a friend. If you ask, “Should I bring a jacket?” their answer depends on context. Do they know where you are? What the weather is like? What you're planning to do? Without that information, the best they can do is guess. It’s the same for AI. This is where context engineering comes in.

Instead of focusing only on the user's prompt, context engineering focuses on the entire system around the AI. It involves building workflows and connecting data sources so the AI has a rich, relevant understanding of the situation before it even begins to generate a response.

Imagine a chef in a kitchen. Prompt engineering is like giving the chef a very precise recipe. Context engineering is like making sure the kitchen is fully stocked with fresh ingredients, the knives are sharp, and the ovens are preheated. It also includes giving the chef a note that says, “The customer is allergic to peanuts.” The recipe is vital, but the prepared environment is what ensures a successful meal.

While related, the two concepts have different scopes. Prompt engineering is about the input, while context engineering is about the environment.

Consider a practical example. You have a customer support chatbot on your website. A user asks, “Where is my order?”

• A prompt engineering approach would focus on training the AI with many examples of how users might ask this question. But the AI itself can’t answer. It would have to reply, “I don’t have access to order information. Please provide your order number.”

• A context engineering approach designs the system so that when the user asks the question, the chatbot can automatically access the company’s order database, find the customer’s recent order based on their login information, and retrieve the shipping status. All this information becomes the context. The system then gives this context to the AI along with the original prompt. The AI can then generate a genuinely helpful response: “Your order #12345, containing one blue sweater, shipped this morning via carrier XYZ. The tracking number is ABC987.”

Ultimately, context engineering is what makes AI practical for real-world applications. It transforms a powerful text generator into a knowledgeable assistant that can understand not just what you say, but what you mean.

Mastering how to provide context is a key step in leveraging AI effectively. By building systems that feed AI the right information, we can unlock its true potential to solve complex problems.