hmm i see. i suppose i just don’t consider that to be much work on my part, which is likely a factor in why chat gpt and others haven’t been very interesting to me from the get go. but to each their own in that regard
hmm i see. i suppose i just don’t consider that to be much work on my part, which is likely a factor in why chat gpt and others haven’t been very interesting to me from the get go. but to each their own in that regard
Right, for most superficial factual queries there is almost always a page. The problem is when there isn't. If the Large Language Model doesn't have pages or references it will tell you, if a search engine doesn't it will still return sponsored pages, and best guesses. If it didn't you'd go somewhere else which would undermine it's revenue model. Which is why it always returns results. The query I gave was a simple misspelling, with an LLM you can drill down to get a much more definitive result, with a search engine you can't. You can't even search within results... it's all a question of how much you value your time...
>>> i just don’t consider that to be much work on my part
This is where many of us have blind spots. We think that our personal experience with findability and the use of AI to help, is simplistic - often one and done, or at least we hope for one and done. This is not what happens in business and enterprise discovery while performing work tasks. It is a conversation, not a mono-search. As such, search engines are terrible at this. Whereas, generative AI can help workers not only put their fingers on the information they need, but it can shape the narrative to understand that information.
We cannot chat our way to hyper-productivity. But we can engage with information far better and faster with chat AI.
hmm i see. i suppose i just don’t consider that to be much work on my part, which is likely a factor in why chat gpt and others haven’t been very interesting to me from the get go. but to each their own in that regard
Right, for most superficial factual queries there is almost always a page. The problem is when there isn't. If the Large Language Model doesn't have pages or references it will tell you, if a search engine doesn't it will still return sponsored pages, and best guesses. If it didn't you'd go somewhere else which would undermine it's revenue model. Which is why it always returns results. The query I gave was a simple misspelling, with an LLM you can drill down to get a much more definitive result, with a search engine you can't. You can't even search within results... it's all a question of how much you value your time...
>>> i just don’t consider that to be much work on my part
This is where many of us have blind spots. We think that our personal experience with findability and the use of AI to help, is simplistic - often one and done, or at least we hope for one and done. This is not what happens in business and enterprise discovery while performing work tasks. It is a conversation, not a mono-search. As such, search engines are terrible at this. Whereas, generative AI can help workers not only put their fingers on the information they need, but it can shape the narrative to understand that information.
We cannot chat our way to hyper-productivity. But we can engage with information far better and faster with chat AI.