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[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(26) | [
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] | p | And it can identify all the different subtasks that are involved in it and then eventually show you the action. | [
"tags",
"tags/llm"
] | 13,672 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(26) | [
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[
"LLM - Qdrant",
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] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(26) | [
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[
"LLM - Qdrant",
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] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(26) | [
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"tags",
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[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(26) | [
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"tags",
"tags/llm"
] | 13,676 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(26) | [
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[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(27) | [
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] | p | Hamza Farooq: | [
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[
"LLM - Qdrant",
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] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(27) | [
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] | p | This is from a talk that was given by Maxwell a couple of months ago. | [
"tags",
"tags/llm"
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[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(27) | [
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[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(27) | [
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] | p | Because people don’t actually use search as what we used. | [
"tags",
"tags/llm"
] | 13,681 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(27) | [
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] | p | Because again, also because of GPT coming in and the way we have been conversing with our products, our search is getting more coherent, as we would expect it to be. | [
"tags",
"tags/llm"
] | 13,682 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(27) | [
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[
"LLM - Qdrant",
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] | p | It often falls too short because a lot of companies, 99.99% companies, I think they are just stuck on elasticsearch because it’s cheaper to run it, it’s easier, it’s out of the box, and a lot of companies do not want to spend the money or they don’t have the people to help them build that as a product, as an SDK that is available and they can implement and starts working for them. | [
"tags",
"tags/llm"
] | 13,684 |
[
"LLM - Qdrant",
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] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(27) | [
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] | p | And the biggest thing is that there are complex search is not just one query, it’s multiple queries, sessions or deep, which requires deep engagement with search. | [
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[
"LLM - Qdrant",
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] | p | And what I mean by deep engagement is imagine when you go to Google right now, you put in a search, you can give feedback on your search, but there’s nothing that you can do that it can unless you start a new search all over again. | [
"tags",
"tags/llm"
] | 13,686 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(28) | [
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] | p | Hamza Farooq: | [
"tags",
"tags/llm"
] | 13,687 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(28) | [
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] | p | In perplexity, you can ask follow up questions, but it’s also a bit of a broken experience because you can’t really reduce as you would do with Jarvis in Ironman. | [
"tags",
"tags/llm"
] | 13,688 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(28) | [
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] | p | So imagine there’s a human aspect to it. | [
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[
"LLM - Qdrant",
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] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(28) | [
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[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(28) | [
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] | p | So this is an example of a copilot which we have been working on. | [
"tags",
"tags/llm"
] | 13,691 |
[
"LLM - Qdrant",
"Transcript:"
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[
"LLM - Qdrant",
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] | p | Cool copilot Carlos was asking, what about downtime? | [
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[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(29) | [
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] | p | When it comes to these LLM services. | [
"tags",
"tags/llm"
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[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(30) | [
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"tags",
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[
"LLM - Qdrant",
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] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(30) | [
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[
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] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(30) | [
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[
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] | p | If you have a production level system running on Chat GPT, you’re going to learn within five days that you can’t run a production system on Chat GPT and you need to host it by yourself. | [
"tags",
"tags/llm"
] | 13,699 |
[
"LLM - Qdrant",
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] | p | And then you start with hugging face and then you realize hugging face can also go down. | [
"tags",
"tags/llm"
] | 13,700 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(30) | [
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] | p | So you basically go to bedrock, or you go to an AWS or GCP and host your LLM over there. | [
"tags",
"tags/llm"
] | 13,701 |
[
"LLM - Qdrant",
"Transcript:"
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] | p | So essentially it’s all fun with demos to show oh my God, it works beautifully. | [
"tags",
"tags/llm"
] | 13,702 |
[
"LLM - Qdrant",
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] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(30) | [
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"tags",
"tags/llm"
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[
"LLM - Qdrant",
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] | p | And the eventual solution is to have dedicated support to it. | [
"tags",
"tags/llm"
] | 13,704 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(31) | [
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[
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[
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[
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[
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] | p | Can I just give you an example? | [
"tags",
"tags/llm"
] | 13,710 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(33) | [
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] | p | Recently we came across a new thing, the token speed. | [
"tags",
"tags/llm"
] | 13,711 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(33) | [
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"tags",
"tags/llm"
] | 13,712 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(33) | [
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[
"LLM - Qdrant",
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] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(33) | [
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[
"LLM - Qdrant",
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[
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"tags/llm"
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[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(33) | [
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"tags",
"tags/llm"
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[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(33) | [
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[
"LLM - Qdrant",
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[
"LLM - Qdrant",
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] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(34) | [
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] | p | So to generate an answer can take up to three minutes. | [
"tags",
"tags/llm"
] | 13,721 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(35) | [
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[
"LLM - Qdrant",
"Transcript:"
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[
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[
"LLM - Qdrant",
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] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(35) | [
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[
"LLM - Qdrant",
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[
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[
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] | p | So Mike has another question coming through in the chat. | [
"tags",
"tags/llm"
] | 13,728 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(35) | [
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"tags",
"tags/llm"
] | 13,729 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(35) | [
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"tags",
"tags/llm"
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[
"LLM - Qdrant",
"Transcript:"
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] | p | So to add on to what Mike is saying, it’s almost like every single piece of your travel or your itinerary would be prefaced with, oh, I like my flights at night, or I like to sit in the aisle row, and I don’t want to pay over x amount, but I’m cool if we go anytime in December, et cetera, et cetera. | [
"tags",
"tags/llm"
] | 13,731 |
[
"LLM - Qdrant",
"Transcript:"
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"tags",
"tags/llm"
] | 13,734 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(36) | [
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] | p | And you can preference all of these different searches with what you were talking about. | [
"tags",
"tags/llm"
] | 13,735 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(37) | [
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"tags",
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[
"LLM - Qdrant",
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] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(37) | [
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"tags",
"tags/llm"
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[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(37) | [
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] | p | So I think that’s a great point. | [
"tags",
"tags/llm"
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[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(37) | [
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] | p | And I will tell you about a company that we have been closely working with. | [
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[
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] | p | It’s called Tripsby or Tripspy AI, and we actually help build them the ecosystem where you can have personalized recommendations with private discovery. | [
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[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(37) | [
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] | p | It’s pretty much everything that you just said. | [
"tags",
"tags/llm"
] | 13,741 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(37) | [
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[
"LLM - Qdrant",
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] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(37) | [
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[
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] | p | And it sort of takes audio and text, and you can converse it through WhatsApp, you can converse it through different ways. | [
"tags",
"tags/llm"
] | 13,744 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(38) | [
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[
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] | p | So they have gone ahead and built something, but with more user expectation. | [
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[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(38) | [
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] | p | Imagine you’re trying to book a hotel, and you also get an article from New York Times that says, this is why this is a great, or a blogger that you follow and it sort of shows up in your. | [
"tags",
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] | 13,748 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(38) | [
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] | p | That is the strength that we have been powering, that you don’t need to wait or you don’t need to depend anymore on just the company’s website itself. | [
"tags",
"tags/llm"
] | 13,749 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(38) | [
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[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(39) | [
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[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(39) | [
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[
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[
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] | p | I think another example of this would be how I love to watch TikTok videos and some of the stuff that pops up on my TikTok feed is like Amazon finds you need to know about, and it’s talking about different cool things you can buy on Amazon. | [
"tags",
"tags/llm"
] | 13,755 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(40) | [
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[
"LLM - Qdrant",
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[
"LLM - Qdrant",
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[
"LLM - Qdrant",
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[
"LLM - Qdrant",
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] | p | Well, so, okay. | [
"tags",
"tags/llm"
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[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(42) | [
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[
"LLM - Qdrant",
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] | p | Carlos had a follow up question before we jump into the next piece, which is around the quality of these open source models and how you deal with that, because it does seem that OpenAI, the GPT-3 four, is still quite a. | [
"tags",
"tags/llm"
] | 13,765 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(43) | [
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[
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[
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] | p | So what we suggest is have open llms as a backup. | [
"tags",
"tags/llm"
] | 13,768 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(43) | [
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"tags/llm"
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[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(43) | [
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"tags",
"tags/llm"
] | 13,770 |
[
"LLM - Qdrant",
"Transcript:"
] | /tags/llm/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(43) | [
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] | p | What we’ve done is that when we’re building large scale products, we basically tend to put an ecosystem behind or a backup behind, which is like, if the token rate is not what we want, if it’s not working, it’s taking too long, we automatically switch to a redundant version, which is open source. | [
"tags",
"tags/llm"
] | 13,771 |
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