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I’m someone who hasn’t jumped onboard this space—isn’t this “prompt engineering” concept just for OpenAI’s ChatGPT or is there some broader reason to expect pro
by malwrar 4y ago
I’m someone who hasn’t jumped onboard this space—isn’t this “prompt engineering” concept just for OpenAI’s ChatGPT or is there some broader reason to expect prompt engineering to be relevant? I’m extremely hesitant to rely on ChatGPT or its current interface as a tool since it exists at the mercy of a massive profit-hungry corporation. It feels too early to start inventing formal concepts against a single black box API.
- zoover2020 4y agoIs is relevant for all LLMs, for instance if you're asking Stable Diffusion to generate an image of a castle. Composing the right prompts here will allow you to obtain actually what you want versus, let's say, a castle as drawn by a toddler (okay maybe that's what you want, but that's not the point)
- williamcotton 4y agoOne of the emergent properties of large language models is that they allow for "in-context learning" by providing "zero-shot", "one-shot", "few-shot" training examples. This seems to be consistent across all sorts of transformer based models with language capabilities. Certain kinds of prompts result in certain kinds of completions and "prompt engineering" is a collection of methods related to encourage a specific completion. For example, prompts that do not contain enough facts about a desired question will result in "hallucinations", whereas prompts that have additional context programmatically added to the question will result in more reliably factual completions. I find it useful to think about this grounded in terms of the analytic-synthetic distinction from analytic philosophy. The above guide is an excellent resource! You should read it!
- malwrar 4y agoI’ll give it a read then! Good to hear it’s more generic than I thought.