| ▲ | nyyp 2 hours ago | |
With regard to my personal use of LLMs, I strongly agree with this framing. But to each point: Anthropomorphism: As we are all aware, providers are incentivized to post-train anthropomorphic behavior in their models - it increases engagement. My regret is that instructing a model at prompt time to "reduce all niceties and speak plainly" probably reduces overall task efficacy since we are leaving their training space. Deference: I view the trustworthiness of LLMs the same as I view the trustworthiness of Wikipedia and my friends: good enough for non-critical information. Wikipedia has factual errors, and my friends' casual conversation certainly has more, but most of the time that doesn't matter. For critical things, peer-reviewed, authoritative, able-to-be-held-liable sources will not go away. Unlike above, providers are generally incentivized to improve this facet of their models, so this will get better over time. Abdication of Responsibility: This is the one that bothers me most at work. More and more people are opening PRs whose abstractions were designed by Claude and not reasoned about further. Reviewing a PR often involves asking the LLM to "find PR feedback" and not reading the code. Arguments begin with "Claude suggested that...". This overall lack of ownership, I suspect, is leading to an increase in maintenance burden down the line as the LLM ultimately commits the wrong code for the wrong abstractions. | ||
| ▲ | jimbokun an hour ago | parent | next [-] | |
These engineers are becoming the real life equivalent of this Office Space scene: https://www.youtube.com/watch?v=hNuu9CpdjIo "I HAVE LLM SKILLS! I'M GOOD AT DEALING WITH THE LLMS!" | ||
| ▲ | tcbawo an hour ago | parent | prev [-] | |
> Yes, the AI may have produced the recommendation but a human decided to follow it, so that human must be held accountable It is common and a mistake IMO to rely on the AI as the sole source for answers to follow-up questions. Better verification would have humans sign off on the veracity of fundamental assumptions. But where does this live? Can an AI model be trusted to rely on previous corrections? This seems impossible or possibly adversarial in a public cloud. | ||