AI Decision Trees

Introduction

An AI decision tree is a workflow where each node is a classification step that routes to the next branch. Useful when intent space is large but well-defined.

Beginner analogy: an IVR phone menu, but smart enough to understand free-text answers.

Understanding the topic

Core concepts:

• Each node = LLM classifier with N labels.
• Edges = labels → next node or terminal action.
• Easy to graph, easy to test per branch.
• Cheap LLM at each node; reserve smart LLM for leaves.
• Beats one giant prompt for many-class problems.

Syntax reference

Visual workflow / architecture:

      Intent?
      ┌──────┼──────┐
   Refund  Order  Other
     │      │       │
   Lookup Track   Escalate

Real-world use

Customer-support routers, ticket triage, lead qualification — all classic decision-tree agents.

Best practices

• Test each branch separately.
• Promote frequent leaf actions to skills.

Common mistakes

• Trees that grow too wide — combine rare branches into 'other'.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. When use a decision tree over ReAct?
• Q2. How do you handle 'other' / unknown?
• Q3. Scenario: a branch is hit 60% of the time but you only allocate 10% of dev time to it. Discuss.