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gsvclass OP t1_j9xsgz3 wrote

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

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.

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cthorrez t1_j9xstlw wrote

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.

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gsvclass OP t1_j9xttgi wrote

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.

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