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Overview

How can we help LLMs handle complex tasks more effectively? Decomposed Prompting leverages a Language Model (LLM) to deconstruct a complex task into a series of manageable sub-tasks. Each sub-task is then processed by specific functions, enabling the LLM to handle intricate problems more effectively and systematically. This approach breaks down complexity by:
  • Generating an action plan using the LLM
  • Executing each step systematically
  • Using specific operations like Split, StrPos, and Merge

Example

References

  1. Decomposed Prompting: A Modular Approach for Solving Complex Tasks