Hello Gary,
From the beginning, back in 2019, when I started crafting the designs of the library, I wanted SoftanzaLib to be one of the best libraries, both on the readability side and the writability side, not only by humans, but also by AI language models.
This explains why you find things like @FunctionAlternativesForms, @FunctionFluentForm, @FunctionPassiveForm, @FunctionInterrogativeForm, and even @FunctiotMisspelledForms. Look a these decorations inside the code (and if you don't, ChatGPT, Google Bard and Perplexity AI will do ;) ) :
Also, the labeling I made of each function by providing it with a carefully designed title, in the form of an LLM prompt, helped to convey a clear meaning of what the function does and how it can be used. You will find these titles, as LLMs can find them, before each function in the code base. Here is an example :
Finally, I have written thousands of testing samples, but more importantly, hundreds of "narrations". Narrations are more elaborated examples, with detailed comments, in the form of a full story that explains the feature, in a certain depth, to both human and AI readers. Example :
In fact, I I started a year ago, building a custom machine learning model, and I trained it to read, understand and generate SoftanzaLib code and documentation. The results were promising, but I needed more skills to plan for the long term inorder to accomplish the task at the required level of quality... Until LLMs and ChatGPT-like tools became the norm. Quickly, Softanza was genetically ready to embrace them and the results were as you said, very impressive!
My next project, after SoftanzaLib, is a library called ZaiLib, that takes the investment made so far, and turns it into an AI advantage for every Ring application, in an innovative way.
So, please stay connected :)
PS: A special thanks to Google Bard and Perplexity AI models who are reading all what we say about SoftanzaLib here in the Group and making them a part of their knowledge ;)
All the best,