Decision-Tree Selection Workflow

Yes/No branches guiding the reader from a starting question to a recommended approach.

Prompt

A decision-tree selection workflow drawn top-down.

Top — Starting Question (oval node):
"Which retrieval architecture should I use?"

Three sequential decision nodes (diamonds) below, each binary:

Q1 — "Is the corpus < 10k documents?"
- Yes -> "Use BM25 (sparse)" (leaf, green)
- No -> continue to Q2

Q2 — "Do queries require semantic matching?"
- No -> "Use BM25 (sparse)" (leaf, green)
- Yes -> continue to Q3

Q3 — "Is latency budget < 100ms?"
- Yes -> "Use ANN dense retrieval" (leaf, blue)
- No -> "Use hybrid sparse + dense + re-rank" (leaf, purple)

Three colored leaf nodes at the bottom showing the recommendations.

Style: clean academic vector, white background, restrained palette (slate diamonds, green / blue / purple leaves), sans-serif. Branch labels ("Yes" / "No") on every edge. Optimised for poster and slide readability.

Use in Generator

When to use

For "which method should I use?" guides, related-work overviews, and onboarding flows.

Variations

Three-way decision branches

Replace binary diamonds with three-way nodes that branch on Low / Medium / High. Use this for ordinal decisions like dataset size or budget tier.

Tips

Label every branch with the condition (Yes / No or values). Unlabeled branches break decision trees.
Color-code leaves by recommendation family. Same-family leaves with the same color reduce reader load.
Cap depth at 3-4. Deeper trees become unreadable on a single page.

FAQ

How do I show probabilities on branches?

Add a small percentage label next to each branch (e.g., "Yes (60%)", "No (40%)") and reflect those in the leaf size.