LLMs shine as brisk and remarkably self-assured pair programming sidekicks that untangle intricate coding challenges. While these models can foresee token sequences in code, vigilant human supervision remains necessary for both precision and thorough testing. Their proficiency can ebb when up against libraries developed after their training cut-off dates, which can sometimes throw a wrench into their usefulness.










