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I really didn’t intend to write this article. After writing my article about genetic algorithms as a service, I was planning to write a follow-up about how to perform automated prompt engineering using genetic algorithms. My goal was to be extremely detailed, describing how every single thing in the system would work down to the exact API calls.

When writing the article, I noticed that my focus started to diverge. I went from talking about how to perform prompt engineering to discussing the intricacies of genetic optimization. While this information was undeniably extremely valuable, it would absolutely scare the piss out of any non-technical user who was interested in automated prompt engineering. Thus, I decided to remove that section and create an entire dedicated article on how this process would work.

Now, at this point, you might be asking yourself, what the heck is a genetic algorithm? Well, this article will not describe that in detail. I have written several articles on what they are and why one would use it, so I’ll only give an extremely brief recap. For a detailed guide on what they are, check out the following article.

Essentially, they are biologically-inspired AI algorithms that can optimize any arbitrary function. Multi-objective genetic algorithms work by generating an entire population of solutions each with their own strengths and weaknesses.

This article will instead focus on how I would design a genetic algorithm service. The target audience is meant to be an experienced developer or machine learning engineer who is curious about how these algorithms work under the hood. If you’re not somewhat technical, then you may want to skip this article. 😆 Sorry, it’s just the truth.

Trying to teach people what genetic algorithms are and how they can be valuable to your business is entirely an uphill battle. While there are some fringe groups who would love access to a raw API, most people simply wouldn’t understand why its valuable. Thus, this API would primarily be internal for my own SaaS companies.

Nevertheless, the value it would unlock for many of my ideas would be nothing short of phenomenal. Genetic algorithms have a wide variety of use-cases that I would immediately experiment with. They are especially useful when trying to generate several solutions each with their own pros and cons. Some concrete examples of use-cases for my SaaS products include trading strategy optimization and automated prompt engineering as a service.

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