1. outputs a problem description suitable as input to a general-purpose planner;
2. solves the problem using the general-purpose planner; and
3. converts the output of the planner back to natural language.

Our extensive empirical evaluations indicate that LLM+P is able to generate correct solutions to
many more planning problems than are LLMs on their own. While demonstrated in this paper on
planning problems, this general methodology can be applied to any class of problems for which we
have a sound and complete solver, such as arithmetic problems (by leveraging calculators).

Limitation:
In this paper, we do not ask the LLM to recognize that it has been posed a prompt that
is suitable for processing using the proposed LLM+P pipeline. A valuable future research direction
will be to consider recognizing when a prompt should be processed by LLM+P.

2 Background
LLM+P- Empowering Large Language Models with Optimal Planning Proficiency