# Least-to-Most Prompting for Complex Reasoning in Language Models [Episode-20]

Page: https://stenobird.com/podcast/the-ai-edge-daily-7851444/least-to-most-prompting-for-complex-reasoning-in-language-models-episode-20
Text version: https://stenobird.com/podcast/the-ai-edge-daily-7851444/least-to-most-prompting-for-complex-reasoning-in-language-models-episode-20.md
Podcast: [The AI Edge Daily](https://stenobird.com/podcast/the-ai-edge-daily-7851444)
Published: 2026-07-05T10:32:43+00:00
Episode link: https://shows.acast.com/the-ai-edge-daily/episodes/6a4a32cc04fac73b246d2218
Audio file: https://sphinx.acast.com/p/open/s/69fc41fd669475c1079ad214/e/6a4a32cc04fac73b246d2218/media.mp3
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JSON: https://stenobird.com/v1/public/podcasts/the-ai-edge-daily-7851444/episodes/least-to-most-prompting-for-complex-reasoning-in-language-models-episode-20
Duration seconds: 1366

## Resource

This episode explores Least-to-Most Prompting , a powerful prompt engineering technique that enables large language models (LLMs) to solve increasingly complex problems by breaking them into manageable subproblems. Learn how this two-stage reasoning framework first decomposes difficult tasks and then solves them step by step, using previous answers to build toward the final solution. The discussion compares Least-to-Most Prompting with Chain-of-Thought prompting, highlighting its superior performance on mathematical reasoning, symbolic manipulation, and compositional generalization tasks. Discover why this approach dramatically improves AI reasoning without requiring additional model training or fine-tuning, making it one of the most influential prompting techniques for building reliable, production-grade AI systems. Hosted on Acast. See acast.com/privacy for more information.

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## Transcript

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