I’ve been diving deep into the world of generative AI for coaches, consultants, and experts. Now, I want to share with you three simple, yet powerful strategies that have worked wonders for me. They’re easy to understand and can really help give your business that extra boost it needs.

So, are you ready to give your business a little AI magic? Let’s get going!

Hey guys it’s Adrian here. If you appreciate my content consider hitting the like button or sharing this article. It’s the only way the algorithm really notices me.

Python programming

I never thought I’d say this but learning a little bit of programming can help your productivity with generative AI.

Taking some time out of your day every day to sink your teeth into the foundations of AI, programming, can take your results to the next level.

While building applications or reading the TensorFlow getting started guide isn’t mandatory it can give you a deeper understanding of the AI you are using. AI models are built on the back of programming logic. If you can understand part of this logic you can better predict the result you are getting.

For example, Hugging Face has a large repository of open source models where you can log in and read the code of the various models. Allowing you to understand the pros and cons.

Mathematics and statistics

Some of you are going to hate me but I have to say this. Math is important.

As most of you know the way LLMs work is through the prediction of the next token based on the probability of the algorithm.

Generative AI relies heavily on mathematics and statistics to write the algorithms that enable these powerful technologies. It would help you to learn these if you want to go deep into these models.

As an entrepreneur or professional using Gen AI these are not mandatory. Though learning just the basics of statistics in machine learning will allow you to reach published benchmarks for each of the new AI tools. So you’ll have the ability to evaluate the quality of what you are using.

Machine learning and deep learning

It’s interesting how most AI use cases all have the same technology supporting them. That’s machine learning and deep learning.

So what’s the difference? Isn’t it all just AI?

Not quite.

Machine learning has been around for quite some time. Being used in our apps and websites for decades now. ML is basically bundles of algorithms that are used to give a prediction of an outcome based on data it’s been fed. ML models (or algorithm bundles) generate predictions based on statistical equations being run like linear regression or clustering.

What separates ML from deep learning is the amount of control. While ML is mostly developed by man, deep learning updates it’s own algorithms based on past iterations. Coding itself in a sense.

Data processing and visualization

A common moniker of AI is “garbage in. garbage out”. Highlighting the importance of data processing in generative AI.

Before large language models even get to analyzing data to create predictions there is a long process of grooming data. Traditionally ML algorithms have been notoriously brittle.

Bad data equals a bad day. Resulting in predictions you can’t trust producing more work for the engineers.

Data scientists and engineers are employed to go over the data about to be fed to the model with a fine toothed comb.

First, they get an “intuition” about the data. Generating visuals to understand what exactly can be used for the AI.

From there bad values are removed and formatted for input.