> ## Content Index > Fetch the complete content index at: https://freshai.ghost.io/llms.txt > Use this file to discover other available public pages before exploring further. # You ordered the slop - URL: https://freshai.ghost.io/you-ordered-the-slop/ - Published: 2026-07-22T23:45:42.000Z - Updated: 2026-07-24T23:56:59.000Z - Description: Why the same AI chat agent gives one person a useless answer and another exactly what they needed. Most of the slop you're served is slop you ordered. - Author: DM - Tags: ai, context, primer, post-1 Spend ten minutes on any forum where people talk about AI and you'll meet the same word over and over: slop. The model makes things up, misses the point, can't be trusted. Slop. It's a real complaint, and I'm not here to tell you it isn't. I'm here to confess something that took me embarrassingly long to figure out. Most of the slop I've ever been served was slop I ordered. Before we get into the why, let me show you one of the times I expected a 5-course Michelin meal and got a microwave TV dinner. Early last year I had the brilliant idea to combine two budding interests: coding with AI and options trading. I opened my Cursor IDE with the arrogant, ignorant confidence of a man who believes he is the first person to ever think of this, and that early retirement is exactly one prompt away. Side note: you don't know how hard I'm cringing while typing this out. *"Help me find a trading edge, then codify it, then run it to automatically find optimal options entry and exits for maximum profit."* Excitement intensified as code populated automagically, as if it were under a money-making spell. Spoiler alert: I'm still working for a living. I tried every other angle I could think of. Different models, more "thinking," a ChatGPT session, a Claude session, all with the same underwhelming results. I ended my weeks-long pursuit with the only conclusion my ego would allow: it must be the kitchen. I had clearly ordered the 8-figure bank account with a side of vacation home in Hawaii, and what I was served was a pat on the back and a "good luck." It wasn't until a recent deep dive into agentic systems that I finally understood what actually happened at that table. I got back exactly what I ordered. Slop. ## Meet the staff There are plenty of "staff members" working in the agentic kitchen, but this post only needs two of them: the waiter and the chef. **The waiter** (the harness) is the chat window most of you know, the thing you actually type into. Claude, ChatGPT, Gemini, the desktop apps, the Cursor agent sidebar. Those are the waiters. Their job is to take your order, carry it back, and return with a plate. Waiters are polished, fast, and very invested in the meal going well. **The chef** (the LLM) is the model doing the actual cooking, behind a door you never walk through. The chef only ever reads a ticket. You never speak with it directly.\* Not once. Everything you believe about "the AI" being smart, dumb, creative, or uninspiring is really about the chef. You tell the waiter, the waiter converts your order into lingo the kitchen understands, and a plate comes back out. Every problem in this post lives in that gap between the table and the kitchen. \* *Even when you skip the chat app and call a model through code or an API, it still receives a ticket, not a conversation with you.* ## The "order," a.k.a. "the context" *Let's talk about context, baby / let's talk about you and me / let's talk about all the good things and the bad things that may be / let's talk about context...* So what is the order, exactly? Everything you hand over for a single request has a name in AI terms: **context**. Context is every scrap of information the chef gets to cook with for *this* plate. Your prompt is the obvious part, but it's not the only part. The back-and-forth you've already had in the session is context. So is a file you paste in, a spreadsheet you attach, a screenshot you drop. Anything a tool goes and fetches on your behalf (more on tools later) gets carried back to the kitchen as context too. The chef also cooks from a second thing: everything it learned in training. But that half is already set. Same chef, same training, same for every diner in the restaurant. The order is the only half you control. So when the meal comes out wrong, the useful question isn't "why is this chef so bad?" It's "what did I actually put on the ticket?" ## The waiter's job is not to interrogate you Now, a waiter angling for a big tip takes the order with as little friction as possible. They will not grill you about what you meant. They'll ask a clarifying question only if they absolutely have to. Otherwise, and this is the main point, the waiter and the chef will quietly infer what you probably meant and send out something plausible. You said "food." Between them they decided you meant a sandwich. Nobody checked. Which could be fine. Maybe you're happy with a sandwich. But notice what's missing from the whole interaction: *there was no error message.* An under-specified order does not come back as "I'm not sure what you meant, can you give me more details?" It comes back as a confident, competently made dish that answers a question you didn't quite ask. The failure is completely silent. The guess made on your behalf is invisible, baked right into the plate, and served at exactly the same confidence as everything else. Sit with that for a second, because it reframes a lot of bad AI answers you've gotten. There's a good chance many of them weren't the AI failing. They were an order it had to guess at, and you never saw the guess. Often you didn't even notice the dish was wrong; you took it at face value and assumed the plate was complete and correct. This is where the familiar complaints begin. "The AI lied to me." "AI hallucinates." "The AI is so confidently wrong." Those feel like character flaws in the machine. Mostly, they're the kitchen filling in blanks you left, because filling in blanks is the job. ## What the chef (LLM) actually is To understand why the chef fills in blanks instead of flagging them, you have to know what it actually is, and it's not what most people picture. The LLM is not a database. It is not looking your answer up in some vast reference and reading it back. What it does is **predict**. Given everything on the ticket (the context), it produces the statistically likely next piece of text, one token at a time, based on the patterns in its training. It's a pattern engine, not a lookup table. Train a model on baking fundamentals and it can tell you what probably goes in a German chocolate cake without ever having seen your grandmother's recipe card. It's predicting what belongs there, not retrieving it. That single fact explains the confident wrongness. The chef is producing a *plausible continuation*, not retrieving a verified record. "Fluent," "confident," and "correct" are three separate things that usually travel together. Usually. When they come apart, you get something that reads beautifully and is simply untrue. The chef also predicts from learned patterns rather than checking a live reference, so its built-in knowledge is frozen at the point training ended. It has no window into right now. Not today's prices, not this week's news. So how does it get anything current, or anything it was never trained on? This is where the waiter and the chef pull out the "phone-a-friend" card: a tool call. ## How the harness and the LLM get more context A tool is something the waiter can go do to gather context you didn't hand over at the table. Think of questions like "what's the price of gas," "are there errors in my application logs," or "what's in the news this week." Some tools come built into the harness; others need installation, setup, and configuration before the kitchen can use them. The most common one is *web search*, and it's also the tool that causes people to overestimate AI the most. When the waiter "looks something up," it is not ingesting the entire internet. It's running a bounded search and reading back a limited slice, and the provider decides how big that slice is. Not you, and not the model. These limits are real and published.\*\* The waiter can step outside to check the weather, but it's a glance out the window, not a meteorology degree. \*\* *Anthropic's developer docs let you cap how many searches happen per request, and going over the cap returns an error, not another result. OpenAI's web-search tool caps what it pulls in at 128k tokens, even on models that can handle bigger context windows.* ## Better context can beat a better LLM Here's where people reach for the wrong lever. When the meal disappoints, the instinct is to get a fancier chef: a bigger LLM, a more expensive tier, "let it think harder." A stronger model can cook the same order a little better. But curating what's on the ticket tends to move the needle more, because the gap between "food" and a described meal is far wider than the gap between a good chef and a great one. There's a stranger wrinkle, too. Researchers have built specific tasks where a bigger model, or a longer chain of reasoning, actually *lowers* the quality of the output. In one 2025 study, both Claude and OpenAI's reasoning models got less accurate the longer they were allowed to think. You know the feeling. You finish a multiple-choice test early, go back to review, second-guess your gut, and change a right answer to a wrong one. That doesn't mean big models and deep reasoning don't have their place. It means the right model and the right effort are things you choose per task, not a dial you leave pinned at max. ## I get it, I need more context. Show me some examples ### The return of the get-rich-quick scheme Let's take my prompt back out and slow it down: *"Help me find a trading edge, then codify it, then run it to automatically find optimal options entry and exits for maximum profit."* Watch what the waiter didn't do with it. It didn't come back and ask: an edge relative to what? How much capital are you putting in? How much can you stand to lose? Over what horizon? Do you want me to invent a strategy, or build one you already believe in? It asked none of that. That was never its job. So the kitchen served up a plausible mashup of every "automated options strategy" ever posted online. Confident, polished, and something I would never in a hundred years run with real money. Notice what was missing. It wasn't some magic edge the model was hiding from me. *What was missing was me.* My risk tolerance. My capital. My actual definition of "edge." My time horizon. And the biggest one: I wanted it to *codify a strategy, not conjure one*. I needed to bring the thesis and let the model build the machinery around it, not ask the model to be the quant. None of that is researchable. None of it existed anywhere but my own head, the waiter never asked, and the chef couldn't tell it was missing. Here's the same subject, ordered the way I'd order it now: "I have $X I'm willing to risk, I can stomach a Y% drawdown, and I trade on a Z horizon. My thesis is \[specific edge\]. Help me pressure-test that thesis, then codify the entry and exit rules so I can backtest it." Same ingredients on the shelf, completely different meal. And now the chef is doing the thing it's genuinely good at instead of guessing at a wish. One honest caveat, then I'll move on: even a perfectly ordered trading bot is part luck. A real market edge is partly unknowable, and no amount of context changes that. Context doesn't buy you a *winning* strategy. It buys you *your* strategy instead of the internet's average of one. I'm not saying AI can or can't beat the market. The lesson here is the order. ### I don't need a realtor, I have AI The trading bot was missing context only I could put on the ticket: my risk, my capital, my thesis. Real estate exposes the opposite gap: facts that exist out in the world but never make it onto the waiter's ticket. Take the vague orders: "I want to sell my house, what's it worth?" or "I want to buy this house, is the listed price worth it?" The first improvement isn't more detail. It's **sequence**. - First: "research the metrics appraisers and realtors actually use to justify a home's value." - Then, only after that framework is on the table: "okay, based on that, here are the home's details and the comps that sold in the neighborhood in the last N months." Make the yardstick before you hand it the thing to measure. That's a move you can lift and reuse anywhere. But there's a gotcha in this example that can send your harness happily off to serve you a wrong answer. By default, without a real-estate-specific data source, don't assume the chat has a live line into MLS, county records, or the nonpublic details behind a sale. You might find what a home sold for online. What you won't find is how many offers there were, the inspections, the disclosures, the concessions, the commissions, the seller's situation, all things that shaped the final selling price. If you can't reach it, neither can the waiter. The answer on your plate is built only from context that actually made it to the kitchen; the LLM has no special reach into datasets you don't have access to. One additional side note here. It's tempting (even for me) to feel like AI endows you with the superpower of instant expertise in anything. But the difference between a surgeon who's practiced for 30 years and one who just finished residency isn't where they graduated. It's the boots-on-the-ground experience, built slowly through all the edge cases and variables that universities just can't teach. AI is a game-changing resource and tool, but it has yet to completely replace expertise earned over time. ## The context Goldilocks zone (when context becomes too much of a good thing) Missing context is only one failure mode, though. You can also bury the order. This one surprises even experienced prompters: *too much* context hurts too. Every time you reply in a chat session, the whole conversation goes back to the kitchen as one big ticket (modern systems cache and summarize, but that's beyond this post). And the longer that ticket gets, the thinner the chef's attention spreads across it. Details buried in the middle, or mentioned many revisions ago, start slipping off the plate. This happens well before any hard limit is reached. The waiter isn't out of paper. The waiter is overloaded. So there's a sweet spot. Hand over a twelve-page dossier for a sandwich and you've buried the one line that mattered under a mountain of lines you didn't need. This context, as a girl stealing porridge from a baby bear once put it, should be *just right*. ## TL;DR: where do I go from here? Before you send the slop back to the kitchen, read the ticket. The waiter doesn't need more words. The waiter needs to know what the meal is for, who's eating, what happens after, and what you can't afford to get wrong. That's the whole move: you're not talking to an oracle, you're talking to a very polite waiter who will never, ever admit it didn't understand your order. Next post: how to build the order with purpose. Breaking a big ask into courses, and watching the context fill up in front of you.