对比图 / 思维导图
Decision-Tree Selection Workflow
Yes/No branches guiding the reader from a starting question to a recommended approach.
完整 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.立即试用此 Prompt适用场景
For "which method should I use?" guides, related-work overviews, and onboarding flows.
变体
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.
使用建议
- 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.
常见问题
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.
