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Least-to-Most Prompting(LtM)
Progressive problem decomposition from simple to complex components
🎯 30-Second Overview
Pattern: Problem decomposition strategy that solves complex tasks by breaking them into simpler subproblems solved sequentially
Why: Enables tackling complex problems by building solutions incrementally from simple to complex components
Key Insight: Decompose complex problem → Solve simplest first → Use previous solutions → Build up to final answer
⚡ Quick Implementation
📋 Do's & Don'ts
🚦 When to Use
Use When
- • Complex problems with clear hierarchical structure
- • Multi-step reasoning with dependencies
- • Mathematical proofs and derivations
- • Programming problems with modular solutions
- • Compositional reasoning tasks
Avoid When
- • Simple problems solvable in one step
- • Highly interconnected problems without clear decomposition
- • Real-time applications requiring immediate answers
- • Problems where context accumulation hurts performance
- • When Chain-of-Thought is sufficient
📊 Key Metrics
💡 Top Use Cases
References & Further Reading
Deepen your understanding with these curated resources
Academic Papers
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models (Zhou et al., 2022)
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022)
Decomposed Prompting: A Modular Approach for Solving Complex Tasks (Khot et al., 2022)
Maieutic Prompting: Logically Consistent Reasoning (Jung et al., 2022)
Contribute to this collection
Know a great resource? Submit a pull request to add it.
Least-to-Most Prompting(LtM)
Progressive problem decomposition from simple to complex components
🎯 30-Second Overview
Pattern: Problem decomposition strategy that solves complex tasks by breaking them into simpler subproblems solved sequentially
Why: Enables tackling complex problems by building solutions incrementally from simple to complex components
Key Insight: Decompose complex problem → Solve simplest first → Use previous solutions → Build up to final answer
⚡ Quick Implementation
📋 Do's & Don'ts
🚦 When to Use
Use When
- • Complex problems with clear hierarchical structure
- • Multi-step reasoning with dependencies
- • Mathematical proofs and derivations
- • Programming problems with modular solutions
- • Compositional reasoning tasks
Avoid When
- • Simple problems solvable in one step
- • Highly interconnected problems without clear decomposition
- • Real-time applications requiring immediate answers
- • Problems where context accumulation hurts performance
- • When Chain-of-Thought is sufficient
📊 Key Metrics
💡 Top Use Cases
References & Further Reading
Deepen your understanding with these curated resources
Academic Papers
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models (Zhou et al., 2022)
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022)
Decomposed Prompting: A Modular Approach for Solving Complex Tasks (Khot et al., 2022)
Maieutic Prompting: Logically Consistent Reasoning (Jung et al., 2022)
Contribute to this collection
Know a great resource? Submit a pull request to add it.