gsvclass OP t1_j9xrqzq wrote on February 25, 2023 at 9:21 AM

Reply to comment by cthorrez in [P] Minds - A JS library to build LLM powered backends and workflows (OpenAI & Cohere) by gsvclass

I updated my comment. Not sure what you mean here "You would have realized that if you had checked the source I linked"? what source

cthorrez t1_j9xrv1v wrote on February 25, 2023 at 9:23 AM

The source I linked in the comment you linked and then deleted.

gsvclass OP t1_j9xsgz3 wrote on February 25, 2023 at 9:31 AM

Ok I saw that not entirely sure what you think prompting is but its not about getting exact answers or anything like that. As I understand it (however limited) it is about bringing attention to a part of the models latent space closest to where your soluton may fall.

cthorrez t1_j9xstlw wrote on February 25, 2023 at 9:36 AM

People are rushing to deploy LLMs in search, summarization, virtual assistants, question answering and countless other applications where correct answers are expected.

The reason they want to get to the latent space close to the answer is because they want the LLM to output the correct answer.

gsvclass OP t1_j9xttgi wrote on February 25, 2023 at 9:51 AM

While it may seem that way correct answers are always expected but never delivered everything works within a margin of error with humans it's pretty large and not easy to fix. Also "correct" is subjective. LLMs are language models use the knowlede embedded in their wieghts combined with the context provided by the prompt to do their best. The positive thing here is that that the margin of error is actively being reduced withn LLMs and not so with however we did this before.