The mistake is treating "AI" and "LLM" as the same word. A large language model is a remarkable reasoner over language. It is not a forecaster, not an optimizer, not a purpose-built perception system, and not the economical way to classify at volume. The pattern that actually wins on cost, speed, and accuracy is quieter: specialist models do the work, and a language model takes the order, routes it, and reports back in plain English.
That is our whole tagline. AI made output infinite. Judgement stayed finite. The value is not in generating more, it is in choosing better.
Two questions
Start with what you have and what you need out of it. The first question is what you are starting with: a spreadsheet of records, a pile of text, images or video, audio, numbers that move over time, a network of connected things, or how customers behave. The second is what you need back: a prediction, a category, a search result, a decision, or something newly generated.
Answer both and you get a plain-English answer, a real example, the technical pick, and the trap that catches most teams. A spreadsheet of records where you want a predicted number, revenue, risk, who will churn, points to gradient-boosted trees (XGBoost, LightGBM, or CatBoost), not an LLM with the row pasted into the prompt: boosted trees still beat deep learning on tabular data, and they give you a calibrated probability instead of a confident guess with nothing behind it.
Twelve jobs where an LLM is the wrong tool
Every one of these twelve is something teams currently hand to an LLM. Every one has a better answer that is cheaper and faster, and once you have labelled data, usually more accurate. A few examples from the list: forecasting next quarter's demand goes to ETS, Prophet, or LightGBM on lags, not an LLM asked to analyze the data, because it cannot do arithmetic reliably over a long series or hold a notion of seasonality. Spotting a defect on a production line goes to PatchCore, PaDiM, or a small CNN, not a multimodal LLM, because the specialist runs on the device in milliseconds with no round trip and no token cost. Scheduling 400 technicians across 3,000 jobs goes to a constraint solver or MILP, not an LLM, because an LLM-generated schedule looks plausible and quietly breaks the constraints that cannot be broken.
Language models are one family out of fourteen
Six of the 145 approaches in the full field guide are language models. The other 139 are how you forecast demand, catch fraud, read an invoice, spot a defect, route a ticket, and decide who to call. The tool has the full, searchable matrix, filterable by family and by the shape of the answer you need.
Originally published on the 7Versions site.