Least-to-Most Prompting is a simple AI technique that breaks tough problems into easier steps, solving them one by one. Each step uses answers from the previous ones to build toward the full solution, making complex tasks manageable.
This method improves on Chain-of-Thought prompting by first splitting a problem into smaller subproblems, then solving them in order from easiest to hardest. Unlike Chain-of-Thought, where steps are separate, here each solution feeds into the next prompt.
It works in two stages:
Inspired by teaching kids step-by-step, it helps AI handle reasoning tasks like math or logic better than standard prompts.
It boosts AI accuracy on hard problems, like achieving 99.7% on SCAN benchmarks versus 6% with standard prompts, by enabling step-by-step reasoning without extra training. This helps non-experts solve tough tasks reliably, improves generalization to new challenges, and makes AI more practical for real-world use like support bots or math problems.
Start by prompting the AI to break your problem into subproblems, using a few examples like: "Break this into steps: [problem]." Then, solve the first subproblem. For the next prompt, include the problem, prior subproblems, their solutions, and the new one: "Using these: [previous], solve: [next subproblem]."
Repeat until the original problem is solved. Combine with few-shot examples for best results. It's great for complex tasks like customer support queries or code challenges, where steps build logically. [Check each step for accuracy before proceeding.
Problem: "A bat and ball cost $1.10 total. The bat costs $1 more than the ball. How much is the ball?"
Decomposition prompt: "Break into subproblems: [full problem]."
AI: Subquestion 1: How much more does the bat cost than the ball? ($1)
Subquestion 2: If the ball costs X, what is the bat's cost? (X + $1)
Subquestion 3: What is the total? Solve for X.
Solving prompts:
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