# Automatic Prompt Engineer (APE)

import { Callout, FileTree } from 'nextra-theme-docs'
import {Screenshot} from 'components/screenshot'
import APE from '../../img/APE.png'
import APECOT from '../../img/ape-zero-shot-cot.png'

<Screenshot src={APE} alt="APE" />

[Zhou et al., (2022)](https://arxiv.org/abs/2211.01910) propose automatic prompt engineer (APE) a framework for automatic instruction generation and selection. The instruction generation problem is framed as natural language synthesis addressed as a black-box optimization problem using LLMs to generate and search over candidate solutions. 

The first step involves a large language model (as inference model) that is given output demonstrations to generate instruction candidates for a task. These candidate solution will guide the search procedure. The instructions are executed using a target model, and then the most appropriate instruction is selected based on computed evaluation scores. 

APE discovers a better zero-shot CoT prompt than the human engineered "Let's think step by step" prompt from (Kojima et al., 2022).

The prompt "Let's work this out it a step by step to be sure we have the right answer." elicits chain-of-though reasoning and improves performance on the MultiArith and GSM8K benchmarks:

<Screenshot src={APECOT} alt="APECOT" />

This paper touches on an important topic related to prompt engineering which is this idea of automatically optimizing prompts. While we don't go deep in this topic in this guide, here are few key papers if you are interested in the topic:

- [AutoPrompt](https://arxiv.org/abs/2010.15980) - proposes an approach to automatically create prompts for a diverse set of tasks based on gradient-guided search.
- [Prefix Tuning](https://arxiv.org/abs/2101.00190) - a lightweight alternative to fine-tuning that prepends a trainable continuous prefix for NLG tasks. 
- [Prompt Tuning](https://arxiv.org/abs/2104.08691) - proposes a mechanism for learning soft prompts through back propagation.