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As it turns out, correctness very often simply doesn't matter. Or not as much as one would intuitively think. How many shops are there optimizing "business str
by choeger 2y ago
As it turns out, correctness very often simply doesn't matter. Or not as much as one would intuitively think.
How many shops are there optimizing "business strategies" with data that's -essentially- garbage?
- croes 2y agoFor that LLMs are good but I bet some people want to use it for things where correctness is vital.
- scarface_74 2y agoIn that case you use RAG and have it tell you the source.
- dotancohen 2y agoA RAG needs to be implemented by the LLM provider. The simple end user has no idea what that means, even though he will be (incorrectly) using the LLM for a vital purpose.
- scarface_74 2y agoChatGPT does exactly that with its built in runtime and web search. But the LLM provider doesn’t have to do that. Langchain - the Python AI library - and OpenAI’s own library has support for third party tools. It’s up to third parties to build on up of it.
- delusional 2y ago> How many shops are there optimizing "business strategies" with data that's -essentially- garbage? How many of those shops are knowingly optimizing with garbage? I'd argue that most of this data, which I would agree is garbage, is actually processed into seemingly good data through the complex and highly human process of self-deception and lies. You don't tell the boss that the system you worked 2 month on is generating garbage, because then he'll replace your with someone who wouldn't tell him that. Instead you skirt evaluating it, even though you know better, and tell him that it's working fine. If the idiot chooses to do something stupid with your bad data, then that's his problem.