What you’ve said makes me think that LLMs have a great use case in creating and searching documentation but if anyone is calling, it really needs a person to deal with that edge case
Not creating and searching as far as I understand (or as far as I’m willing to allow it to in this case) but more summarizing, truncating, some times of rewording (non-technical parts).
They’re getting better at extracting information out of a closed set of data, but it’s still literally impossible to guarantee that it won’t generate a contradictory or unwanted piece of text that looks very close to the right thing, based off the training data inherent to the model.
But the “best” case is something closed ended where you know what the output is. So cleaning up a tiny piece of code, summarizing something that you provide in its entirety, translating a block of text, that’s all a good use case. Using it to distill the entire web’s information into a chatbot format? Fuck no
The entire problem is people thinking this tool that can turn text input into soup and reliably pull text back out of said soup is something it just is not.
Most of the models I’ve played with before the boom were not instruct models. So you didn’t prompt them and have them churn out slop that sounds like the answer to your question. Instead you just wrote text (story, article heading, etc) and it would continue the pattern. The results were “worse” in quality, but because we only thought to use it a specific way, it felt like a very powerful new tool.
My enthusiasm for this shit has fallen through the floor in 2022 and presently is about 18% through the earth’s outer crust
You can have LLMs draft documentation based on callcentre communication - that’s what I meant by creating
With regards to searching, the thinking models seem really good at finding what you need when you don’t know what exactly to search for
“Semantic search” / “semantic indexing”. Yes. Would be a great thing to optionally have. But you don’t need to hook it up to a prompt and have it spit out natural language output.
It could be just like a standard search with search results, just with a backend that looks at more stuff based on meaning not just explicit word matching. And search engines have worked like this for years to be fair.
But I agree, the general purpose chatbots are probably helpful to get a foothold on looking something up when you don’t really know what it’s called or how to concisely describe it. The problem is that the companies that make them have every incentive to feed you their explanation too, not just point you in the right direction and have you leave their service.
What you’ve said makes me think that LLMs have a great use case in creating and searching documentation but if anyone is calling, it really needs a person to deal with that edge case
Not creating and searching as far as I understand (or as far as I’m willing to allow it to in this case) but more summarizing, truncating, some times of rewording (non-technical parts).
They’re getting better at extracting information out of a closed set of data, but it’s still literally impossible to guarantee that it won’t generate a contradictory or unwanted piece of text that looks very close to the right thing, based off the training data inherent to the model.
But the “best” case is something closed ended where you know what the output is. So cleaning up a tiny piece of code, summarizing something that you provide in its entirety, translating a block of text, that’s all a good use case. Using it to distill the entire web’s information into a chatbot format? Fuck no
The entire problem is people thinking this tool that can turn text input into soup and reliably pull text back out of said soup is something it just is not.
Most of the models I’ve played with before the boom were not instruct models. So you didn’t prompt them and have them churn out slop that sounds like the answer to your question. Instead you just wrote text (story, article heading, etc) and it would continue the pattern. The results were “worse” in quality, but because we only thought to use it a specific way, it felt like a very powerful new tool.
My enthusiasm for this shit has fallen through the floor in 2022 and presently is about 18% through the earth’s outer crust
You can have LLMs draft documentation based on callcentre communication - that’s what I meant by creating With regards to searching, the thinking models seem really good at finding what you need when you don’t know what exactly to search for
“Semantic search” / “semantic indexing”. Yes. Would be a great thing to optionally have. But you don’t need to hook it up to a prompt and have it spit out natural language output.
It could be just like a standard search with search results, just with a backend that looks at more stuff based on meaning not just explicit word matching. And search engines have worked like this for years to be fair.
But I agree, the general purpose chatbots are probably helpful to get a foothold on looking something up when you don’t really know what it’s called or how to concisely describe it. The problem is that the companies that make them have every incentive to feed you their explanation too, not just point you in the right direction and have you leave their service.