Same Problem, Four AI Layers — Lab
Pick a task. See how rule-based AI, classical ML, deep learning, and an LLM each attack the same problem. The right tool is rarely the fanciest one.
An interactive companion to Same problem, four AI layers — pick a practical task (spam, fraud, image classification, customer chat) and see how each of the four layers attacks it differently. The verdict tag "right tool here" is rarely on the most expensive option.
Same problem, four AI layers
Pick a real-world task. See how each of the four AI layers — rule-based AI, classical ML, deep learning, and an LLM — would attack the same problem. The mechanical differences land harder when the task is held constant.
Before you start
Read this short panel first. It tells you what the lab is, what it is trying to make you see, and how you will know if you got there.
🎯 Purpose
Four practical problems are sitting on the page below. For each, four different AI layers are presented as candidate solutions: rule-based AI, classical ML, deep learning, and an LLM. Each layer-card describes the actual approach it would take, what it would need (data, compute, expertise), and an illustrative accuracy band you should expect in 2026. The lab is the comparison between the four cards for the same problem.
💡 What it is trying to make you see
That the answer to "which AI layer should I use?" is never universal — it depends on the problem. For some tasks, a hand-coded rule wins on every metric that matters. For others, a 30-line scikit-learn script beats a million-dollar deep network. For others, only a frontier LLM can really do the job. The companion article gives the conceptual taxonomy; this lab shows you the trade-offs in practice, one task at a time.
✅ What you should understand after playing
After flipping through all four problems, you should leave able to:
- Name a problem where a rule-based approach is the right answer in 2026, and explain why an LLM would be overkill or actively worse.
- Name a problem where a classical ML model still beats deep learning, and explain why (data shape, interpretability, regulation, or all three).
- State the question you should ask before reaching for an LLM: "What is the simplest layer that solves this acceptably?"
If those three are true for you when you leave, the lab did its job. If not, re-read the rule-based card on the spam-detection problem — it is the clearest case.
How to use it — 30 seconds
- Pick a problem. Four buttons further down — spam detection, fraud detection, image classification, customer chatbot.
- Read the four layer-cards. Each describes how that layer attacks the chosen problem, what it needs, and the realistic accuracy band.
- Compare. Watch which layer has the verdict tag "right tool here" — it is rarely the most expensive option.
Pick a problem
Click any problem to see how all four AI layers would attack it.
Lab #5. Companion to AI vs ML vs Deep Learning vs LLMs. Feedback welcome.