AI has been commoditized. Machine learning, once a discipline requiring years of study, is now a prompt away from full implementation.
But "full implementation" is vague. It requires a grading scale. As a TA, mine was simple: if it works, it's a C. Input/output prompting for a quick result will give you that C.
Getting to an A is the data model. The practices behind the data engineering efforts. Data clarity. Data consistency. The deep research into which data points are insight and which are noise.
This was true before LLMs, and it's even more true now. The difference between a black-box output and a system you actually understand is the architectural process that happens before you ever build a pipeline. The story it needs to tell, and for whom.
That's what I do. Not just the final output, but the why, with true Computer Science rigor, so your output always means something.
Please read my bio again. You'll see who's built for this.