Introduction

Have you heard about Automatic Prompt Optimization (APO)?

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Do you work with generative AI LLMs or AI tools for content creation and image generation? Then you must read this.

Here is an example of how automatic prompt optimization might work:

Let’s say you want an LLM to write a poem about a flower. You could give it the prompt, “Write a poem about a flower.” However, this prompt is not very specific. It doesn’t tell the LLM what kind of flower you want it to write about or what the poem should be about. Automatic prompt optimization could help you make this prompt better by making it more specific and detailed.

For example, you could add the following details to the prompt:

  • The poem should be about the beauty of the rose.
  • The poem should use similes and metaphors to describe the rose.

With these details, the LLM will be better able to generate a poem about a rose.

Automatic prompt optimization is a powerful tool that can help you write better prompts. If you are using an LLM to generate text, I encourage you to try using automatic prompt optimization. It can help you get better results.

Advantages

  • You don’t have to hire a person for prompt engineering (people are still willing to pay $375 K for this job to get results.)
  • You don’t have to be an expert at writing prompts to get better results from LLMs. It will automatically rewrite your prompts as per your needs.
  • If you are having AI SaaS, your clients will be happy, when they make better content using this APO.

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Existing Tools

Microsoft has released Promptist for image generation and Structured Prompting, a technique for including more examples in a few-shot learning prompt for text generation.

Promptist & Structured Prompting can be found in Microsoft LMops

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That’s it for this blog.