[a1371]

Prompt engineering doesn't feel like an activity that creates sustainable AI advancement. A prompt may work well with one model, in most situations, but even the best practices seem too experimental.

For their competition to avoid a PR disaster, isn't it better to look in the model? Perhaps observe the weights, when the AI says something that you want to avoid in the future. A safeguard could trigger if the model is going in that direction.

[kang]

> Prompt engineering doesn't feel like an activity that creates sustainable AI advancement.

Chatgpt was created from gpt via prompt engineering? An inverse chatgpt where user answers questions instead of the other way around also has applications.

[rcxdude]

Only in that the very initial version was GPT + prompt. ChatGPT is heavily fine-tuned using both handwritten examples and later on rated examples from its own output. The prompt is still relevant but the fine-tuning is the biggest thing that makes it work like a chatbot.

[dvt]

> Prompt engineering doesn't feel like an activity that creates sustainable AI advancement.

Agreed, it should really be rolled into fine tuning. If you're building a model for PR, for example, it should already be fine tuned so it can't say anything disastrous. Prompt engineering is only really relevant to general-purpose models which aren't that useful to begin with (other than "fun" chatting).

[LZ_Khan]

LLM's are trained in much the same way, so while your point stands, most/all of the tips here are going to be useful for LLM's for at least a year or so.

If a tip was like "use XML tags to give clarity to the model," then it wouldn't be sustainable.