Automatic Prompt Engineer

What is Automatic Prompt Engineer (APE)?

Automatic Prompt Engineer (APE) is a framework for automatically generating and selecting prompts for large language models (LLMs) to improve their performance on various tasks[1][3][5]. The key ideas behind APE are:

- Treating the instruction as the "program" that needs to be optimized[3][5]
- Using an LLM to generate a pool of candidate instructions[1][3]
- Searching over the candidate instructions to find the one that maximizes a chosen score function[3][5]
- Evaluating the quality of the selected instruction by testing the zero-shot performance of another LLM following that instruction[3][5]

How does APE work in practice?

1. An LLM is used as the prompt generator to produce a set of candidate instructions for a given task, based on a few input-output examples[1][2].

2. Another LLM is used as the content generator to actually perform the task following each candidate prompt[2][3].

3. The quality of each prompt is evaluated based on how well the content generator performs the task when following that prompt[2][5].

4. The prompt with the highest evaluation score is selected as the final optimized instruction for the task[1][3].

5. This process can be repeated iteratively, using the best prompt so far to generate new candidates and further refine the instruction[2][5].

Benefits and applications of APE

- Automatically generates high-quality prompts that outperform human-engineered ones on many tasks[1][3][5]
- Makes training LLMs easier and faster by enabling the use of synthetic data instead of manually curated datasets[2]
- Boosts LLM performance by tailoring prompts to specific tasks[2][4]
- Improves few-shot learning by prepending optimized prompts to standard in-context learning examples[5]
- Can steer LLMs towards more truthful, informative and coherent outputs[4][5]
- Has applications in areas like app development, computational linguistics, and prompt-based learning[4]

In summary, Automatic Prompt Engineer is a powerful framework that leverages the capabilities of LLMs to automatically discover effective prompts, enabling more efficient and versatile use of these models. By automating the tedious process of prompt engineering, APE represents a significant advance in the field of generative AI.