After reading numerous search results, it appears clear that “”“fine-tuning”“” involves updating a language model’s parameters, while “”“prompt engineering”“” entails temporary learning during inference. Some business applications may not require fine-tuning or prompt engineering, as ChatGPT’s f…

Fine-tuning : Fine tuning is best for a scenario when you will be using GPT for a specific task repeatedly, where it might not need to learn much aside from the fine tuning you might have done. However, with fine-tuning only possible on lower models, the quality of the output might be more in line w…

I do have some experience with “plugins”, so expanding on what UDM17 said: “Plugins” can provide models with access to external information, such as allowing a model to search the web, use a calculator to solve problems or access a company’s database. The use cases for plugins are pretty much endle…

I like the student analogy you started. I often like to think of LLM as a small child or a young student. With that, let’s say the student is giving an exam for a certain subject. Plugins: Giving student the textbook for the exam. They need to understand the exam question, figure out what informat…

[image] wnsdnd1120: fine-tuning, prompt engineering, and plug-ins Fine-tune → Categorization, Filters Prompt Engineering → Live info, your own facts, aka embeddings Plug-ins → Extending the AI to another SaaS offering, or the generic built-in ones.

Fine-Tuning vs Prompt-Engineering vs Plug-in | each suitable usage

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yiting April 4, 2023, 6:46am 2

Great questions!! I am also researching on this!!
If anybody would provide tips or recommend good reference articles, it would be really appreciated~!!

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Fine-Tuning vs Prompt-Engineering vs Plug-in | each suitable usage

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