To be fair, you would not actually need to know how numpy.linalg.solve works to use pytorch. Solving linear equations is an extremely deep subject on its own, featuring a lot of difficult and sophisticated issues related to numeric stability. Entire books have been written about how to solve Ax=b with awareness of the nature of floating point arithmetic. Machine learning researchers are generally unconcerned with those topics.
As everything in the world of knowledge, this topic too has a fractal nature. There's a difference between what you just said vs "well, optimize it with gradient descent, yoloswag" or "just invert the matrix A". If you say something something pivot selection, Gauss elimination, iterative algorithms, QR, LU factorization, backslash operator, pseudoinverse, under and overdetermined systems and can roughly handwave your way around roughly explaining what these things are about, it's probably already very good for this job. This prof probably wasn't interested in the tiniest of numerical stability details.
