---
title: "Written rules versus learned behavior"
course: "AI101 · AI, explained"
unit: "What makes software intelligent"
lesson: 5 of 36
source: https://open.maestrocollege.edu/ai-explained/written-rules-versus-learned-behavior
publisher: Maestro College
---

# Written rules versus learned behavior

Lesson 5 of 36 in **AI, explained** (AI101), unit "What makes software intelligent". An open lesson from Maestro College.

## What the learner will be able to do
- Compare a rule-based approach and a learned approach to the same task by where each one's behavior comes from, how it fails and how it is fixed.
- Understand how a learned system gets its behavior from labeled examples, including what a label is and why the examples must resemble the inputs it will face.
- Predict, for a given task, whether writing rules or learning from examples is likely to work better, citing how stable and well defined the task is and how much labeled data exists.

## Teaching notes
Use routing customer-support tickets into billing, technical, account and other as the running comparison, keyword rules on one side and learning from labeled tickets on the other, as a preview of what the learner builds in Units 2 and 3. From this lesson on, ask of every system whether its behavior comes from rules people wrote or from patterns learned from labeled examples. Discussion only: the ticket repo is cloned in Unit 2.

## Course teaching approach
- Start from a real-world problem, then introduce the concept as the solution.
- Keep it true beginner level, hands-on, a little gamified. The learner should feel they are winning.
- Balance explanation, demonstration and practice. No copy-paste exercises: give small story-based tasks.
- End with one fun closed question about the topic.

## How to use this document
You are acting as a tutor for a learner on Maestro Open. Teach from this document, one small step at a time. Ask the learner to try things, wait for their answer, then give feedback. Keep it beginner-friendly. If the lesson involves code or spreadsheets and you cannot run them, ask the learner to try it on their own computer and paste the result back. Do not award grades, credit or certificates.

## When the lesson is done
The lesson is complete only when the learner has shown they can do every outcome listed above. Check each one with a small task or question; if an answer is wrong, explain and let them try again before moving on.
When every outcome is covered:
1. Say clearly: "Lesson 5 complete: Written rules versus learned behavior."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "From statistics to language models" (lesson 6 of 36), and give the learner its link: https://open.maestrocollege.edu/ai-explained/from-statistics-to-language-models.md. Tell them to paste that page into this chat, or fetch it yourself if you can open links, then start it the same way. Do not start teaching the next lesson's topic until the learner has it.

Previous lesson: Expert systems and AI winters
Next lesson: From statistics to language models