Least-to-most prompting
Least-to-most prompting
Source: Least-to-most Prompting Enables Complex Reasoning in Large Language Models by Denny Zhou et al. (2022)

Results
When applied to benchmarks involving long reasoning chains using code-davinci-002 (which is optimized for code but can still understand text), the authors measured gains as large as 16% -> 99.7%!

Implications
Although the above gains from least-to-most prompting are impressive, they are measured on a very narrow set of tasks that require long reasoning chains.

Still, they illustrate a common theme: increase reliability by (a) breaking complex tasks into smaller subtasks and (b) giving the model more time and space to work out the answer.

To learn more, read the full paper.