Scaffolded Prompting erects temporary supports for intricate constructions, breaking queries into granular sub-prompts—each advancing the whole while providing contextual scaffolding from priors. As proficiency mounts, scaffolds retract, yielding autonomous completion. This pedagogical layering mitigates overload, fostering mastery in tutorials, multi-module developments, or skill-building sequences.
Examples
User Prompt: Build a novel chapter-by-chapter using Scaffolded. AI Response: Step 1: World-build outline. Step 2: Character arcs. Step 3: Scene drafts. Cumulative: Ch1 integrates all.
User Prompt: Develop a machine learning model progressively. AI Response: Step 1: Data prep script. Step 2: Feature engineering. Step 3: Train/eval loop. Full: End-to-end pipeline.
User Prompt: Learn guitar basics scaffolded. AI Response: Step 1: Chord shapes. Step 2: Simple strumming. Step 3: Song application. Progression: From tabs to full play.
User Prompt: Research paper via steps. AI Response: Step 1: Thesis. Step 2: Sources annotated. Step 3: Sections drafted. Final: Cohesive manuscript.
Advantages
- Mitigates complexity paralysis by incrementalizing, ensuring steady advancement.
- Facilitates error localization, as steps are isolatable for fixes.
- Mirrors human learning curves, effective for education or onboarding.
- Modular design allows pausing/resuming, suiting episodic interactions.