After 2 years in a professional kitchen, I found myself working at a restaurant that was much busier than I was prepared for. That first weekend, I was responsible for frying chicken, lamb, and plating for over 200 people. I was so overwhelmed that I was literally sobbing over the fryer while the tickets kept screaming. Nobody could help me. Everyone was pinned to their own station. I wanted to walk out, but I stayed.
Later that night, as we were cleaning down the stainless steel, the head chef pulled me aside. He didn’t tell me to toughen up or stop crying. He said, “I respect you for staying. I cannot teach you to care and to not give up. But I can teach you the discipline needed to cook better, faster, and more efficiently.”
The lesson he eventually taught me had nothing to do with how fast I could move my hands. It was about mise en place.
Mise en place is a French term meaning “everything in its place.” But that translation undersells it. Mise en place is a philosophy. It is the practice of having your shallots minced, your sauces strained, your tools positioned, and your mind prepared before the first guest ever walks through the door. It’s not just about being organized. It’s about understanding that the work you do before service determines whether service destroys you or whether you destroy service.
Nothing feels worse than ending a 10 hour shift and reflecting on all your mistakes. Similarly nothing is better than knowing that for the last 10 hours you crushed everything thrown at you.
That first weekend at the fryer, I wasn’t failing because I was slow. I was failing because I started the night already behind and tried to move too fast. My station wasn’t set. My mental checklist didn’t exist. I was improvising from the first ticket, and improvisation compounds. One mistake leads to two. Two leads to six. By 6:00 PM, you’re sobbing into a fryer and wondering why you ever thought this was a good career.
In AI implementation, most teams are doing exactly what I did that weekend. They’re rushing to deployment without setting the station first. And they’re wondering why everything falls apart the moment real users show up.
What Mise en Place Actually Looks Like
When you watch a professional cook work a busy station, it looks effortless. Pans move. Plates appear. Nothing burns. But what you’re actually watching is the result of three or four hours of invisible work that happened before you arrived.
Mise en place has three layers, and most people only think about the first one.
The first layer is physical organization. This is the obvious stuff. Your ingredients are prepped and portioned. Your tools are clean and within reach. Your station is wiped down. If you need clarified butter, it’s already in a squeeze bottle at arm’s length. If you need tongs, they’re always in the same spot. You never have to think about where anything is, because everything is where it belongs.
The second layer is workflow design. This is understanding the sequence of your work. If you’re cooking a steak that takes twelve minutes and a sauce that takes three, you don’t start them at the same time. You know which tasks can run in parallel and which ones need your full attention. You know what can sit for a minute and what needs to be served immediately. A good cook has already visualized the entire ticket before they touch a pan.
The third layer is mental preparation. This is the part nobody talks about. Before service, you run through scenarios. What happens if I get three ribeye tickets at once? What if someone orders the fish special and I only have two portions left? What’s my backup if the grill goes down? You’re not hoping things go smoothly. You’re preparing for them to go sideways, so that when they do, you already have a plan.
Most AI implementations skip layers two and three entirely. Teams spend weeks building the model, which is the equivalent of prepping ingredients. Then they throw it into production without ever thinking about workflow or failure scenarios. The model itself might be excellent. But excellence doesn’t matter if the system around it is chaotic.
Setting Your Station for AI
Let me translate these three layers into what they look like for AI and automation work.
Physical organization in AI means information architecture. In a professional kitchen, the person making salads shouldn’t be positioned right next to the deep fryer. The heat and moisture will wilt the greens, creating chaos and ruining the dish. Every station needs proper spacing and specific tools. We ignore this logic in system architecture constantly. Many teams place computationally expensive processes right after a user’s first interaction, which bottlenecks every simple operation that follows. You’re designing a system destined to fight itself. Your data sources, your processing layers, and your output channels all need their own clean space. They shouldn’t interfere with each other.
Workflow design in AI means prompt sequencing and timing. When a cook gets a ticket with a steak, a sauce, and a vegetable, they don’t start all three at the same time. They work backwards from the plate. The steak takes twelve minutes. The sauce takes four. The vegetables take two. So you fire the steak first, then the sauce at eight minutes, then the vegetables at ten. Everything lands at the same time, hot and ready.
The head chef’s job during service is to manage this timing across the entire kitchen. They call out “firing table 12” so the grill cook knows to start the steak. Three minutes later, they call “walking in the sides for 12” so the vegetable station knows to start plating. The chef isn’t doing the cooking. They’re orchestrating the sequence so that every component arrives at the pass together.
This is exactly how complex prompts should be structured. When you give a model a complicated task, you need to tell it what to prioritize and in what order. If you dump every instruction at once with no hierarchy, the model has to guess what matters most. Sometimes it guesses right. Often it doesn’t. It’s like handing a cook six tickets and walking away without telling them which table has been waiting longest.
Instead, chain your instructions. Tell the model what to figure out first, what depends on that answer, and what the final output should look like. Give it the “firing order.” The model works best when it understands the sequence, not just the ingredients.
Mental preparation in AI means anticipating edge cases and failure modes. What happens when a user submits an input you didn’t expect? What happens when your API rate limit gets hit? What happens when the model hallucinates? If you haven’t thought through these scenarios before deployment, you’re going to be sobbing over the metaphorical fryer when they inevitably occur. The best teams I’ve worked with spend more time on “what could go wrong” as they do on “what should go right.”
The 4:00 PM Problem
In most restaurants, 4:00 PM is the danger zone. Lunch service is wrapping up. Dinner prep is supposed to be happening. But if lunch ran long or got chaotic, you’re now behind on dinner prep. And if you’re behind on dinner prep, dinner service will be a disaster. The mistakes compound.
This is exactly what happens with AI deployments that skip mise en place. The initial launch might look fine. Early users are forgiving. Traffic is light. But as usage scales, as edge cases multiply, as the system gets stressed, all the shortcuts you took in the setup phase come back to haunt you. By the time you realize you’re in trouble, you’re already too deep in service to fix it.
There is nothing that ruins a kitchen faster than contamination. Spill raw chicken on a cutting board and you have to stop everything, clean the boards, and throw away any food nearby. Contamination spreads instantly. The consequences are severe. Data hygiene works the same way. If you allow junk content into your flow, if you have polluted context windows or insecure data handling, you’re not just making a mistake. You’re creating toxic debt. Shortcuts and accidental complexities that quietly become permanent defects in your system. If your data foundation is a mess of mystery spreadsheets and zero documentation, adding AI doesn’t solve anything. It just automates the mess at scale.
The most expensive dish in a restaurant isn’t the Wagyu. It’s the dish you have to cook twice. Rushing leads to mistakes, and doing something a second time is always slower than doing it right the first time.
The Lesson That Stuck
I think about that head chef often. He could have told me I wasn’t cut out for kitchens. He could have let me quit. Instead, he taught me that the feeling of drowning isn’t a sign that you’re bad at the work. It’s a sign that you haven’t prepared properly for the work.
Mise en place isn’t about being naturally organized. It’s about accepting that chaos is the default state of any complex system, and that your job is to impose order before the chaos arrives. You don’t rise to the occasion. You fall to the level of your preparation.
Whether you’re dicing shallots or deploying a language model, the principle doesn’t change. Get your station ready. Think through the workflow. Prepare for failure. Then, and only then, are you ready for service.
I’ll see you on the line.
Jacob