the "AI lies" takeaway is way off for those actually using these tools. Calling it a "junior dev faking competence" is catchy, but misses the point. We're not expecting a co-pilot, it's a tool, a super-powered intern that needs direction. The spaghetti code mess wasn't AI "lying", it was a lack of control and proper prompting.
Experienced folks aren't surprised by this. LLMs are fast for boilerplate, research, and exploring ideas, but they're not autonomous coders. The key is you staying in charge: detailed prompts, critical code review, iterative refinement. Going back to web interfaces and manual pasting because editor integration felt "too easy" is a massive overcorrection. It's like ditching cars for walking after one fender bender.
Ultimately, this wasn't an AI failure, it was an inexperienced user expecting too much, too fast. The "lessons learned" are valid, but not AI-specific. For those who use LLMs effectively, they're force multipliers, not replacements. Don't blame the tool for user error. Learn to drive it properly.
“Experienced folks” in this case means folks who’ve used LLM’s enough to somewhat understand how to “feed them” in ways that make the tools generate productive output.
Learning to properly prompt an LLM to get a net gain in value is a skill in it of itself.
Experienced folks aren't surprised by this. LLMs are fast for boilerplate, research, and exploring ideas, but they're not autonomous coders. The key is you staying in charge: detailed prompts, critical code review, iterative refinement. Going back to web interfaces and manual pasting because editor integration felt "too easy" is a massive overcorrection. It's like ditching cars for walking after one fender bender.
Ultimately, this wasn't an AI failure, it was an inexperienced user expecting too much, too fast. The "lessons learned" are valid, but not AI-specific. For those who use LLMs effectively, they're force multipliers, not replacements. Don't blame the tool for user error. Learn to drive it properly.