Dr. Violet Howard explains context engineering, the practice of feeding AI agents the right information and tools to complete tasks. Unlike prompt engineering, which crafts a single instruction, context engineering lets agents dynamically access data sources and tools, turning them into true assistants instead of forgetful conversational partners. She draws on lessons from building over 200 AI automations.
Here are my 6 context engineering lessons after building over 200 AI automations. I've come to realize that context engineering is the most important thing in determining the quality and consistency of the AI systems. So today I'm gonna be going over the 6 most important context engineering lessons that I've learned, breaking it all down and applying it to N8N AI agents. All right, just to start off here with module 1, intro to context engineering, if it's a term that you've never heard before. So context engineering is the art of feeding your AI agent the right information that it needs to complete tasks effectively. Unlike prompt engineering, which focuses on crafting the perfect single instruction, context engineering is about building systems that can dynamically provide relevant information to the agent. So in a nutshell, the agent should be able to receive some sort of message that triggers it. It should understand, I have all of these different tools, I have all of these different sources of data or information that I can look at. Which ones do I actually use to get information to help me answer or do the right task? Now the problem is most AI agents today that aren't using a lot of context engineering it's like having a conversation with someone who forgets everything right after you've said it. And the solution to this is context engineering. Because when an agent is triggered, it's going to initially look right at its system prompt, and that's what it's going to use to determine what to do next. So the solution is, after the agent reads through its system prompt, it should understand, I have these different tools that I can use to get more context, and this is going to transform me from a simple question and answer tool to an actual assistant that can remember things and take action intelligently. And I just wanted to say that this isn't a new concept. It's been around. I think the term context engineering is a little bit more on the newer side, but it's always been around. These are videos I've made multiple, multiple months ago talking about the foundation of AI systems and automations is data and context. And over here was a completely separate video where I talked about context is absolutely everything. The systems are only going to work as well as the data and context you feed in. Data in, data out, garbage in, garbage out. And especially if you're working on systems for a client or something like that, You're really gonna have to leverage their subject matter expertise to train the systems to behave the way an employee in their business would. If you wanna watch the full breakdown, click on that play button right here, and I'll see you guys over there.
