In today’s episode, Jesper asks if news outlets blocking AI scrapers will impact retrieval augmented generation models. I explain that blocked scrapers won’t matter since public data is aggregated elsewhere, though news outlets have valid concerns about uncompensated use. I compare fine-tuning to upgrading appliances versus retrieval augmented generation to adding ingredients, noting RAG’s strength for changing context. Tune in to learn more about advancing AI techniques and how models consume restricted data.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today’s episode, Jesper asks, How do you see the future for retrieval augmented generation AIs when particularly news outlets shut out AI crawlers, scrapers, etc? Okay, so AI crawlers, scraping and crawling bots typically are deployed by a company, they’ve had an identified browser agent right open AIs crawler, and you can and if you want to, you can block those specific crawlers.
However, there’s a bunch of other ones that are pulling the exact same information.
In fact, if you look at common crawl, go to common crawl dot work, you will see that they crawl the entire public internet.
So even if a news outlet says you may not crawl us, you know, a open AI bot, open AI just has to go to common crawl, pull the latest vintage from there, and then use that for processing.
So that’s kind of a fool’s errand trying to block AI system controls from consuming content, especially if you’re already giving it to search engines, right? So if you are allowing Google bot, well, sure, open AI might not then crawl your site, but Google will.
And if Google is going to do it, then guess where that information is going to end up, it’s going to end up in one of Google’s models.
So you really not accomplished anything to the question though, but retrievable augmented generation, how that plays a role.
It’s important to understand the role of retrieval augmented generation.
So let’s, let’s go back to some basics.
When you have an AI model like GPT, for the model that powers the paid version of chat GPT.
There’s a couple different ways to get a model to behave differently.
One is prompting the prompts you give the instructions, the directions, the plain language coding, the more sophisticated you’re prompting, the better the results you will get, you will get out of a big general model like that.
So that’s one area.
It’s just being very good at prompting.
And there’s a whole bunch of ways to do that.
There’s some really advanced studies coming out now that are showing that good prompting can actually outperform some other methods of getting models to work in a certain way.
Fine tuning is sort of the second way.
And this is where you condition a model to answer specific kinds of questions better than the model was originally trained on.
So if you fine tune a model on, say, medical questions, and you just give it a whole bunch of questions and answers, the model may not get any new information that way.
But it’s going to get it’s going to learn how to answer those questions better than whatever medical information was put in in the original model.
I use I like to think of this as like the way you train a dog, right? You train a dog to sniff for drugs, it’s not going to be able to sniff for explosives or earthquake survivors.
But it’s gonna be really good at what you trained it to do.
That’s what a fine tune is.
Retrieval augmented generation is is a library, it’s a database, it’s an add on to a model, which gives the model more context, more more information, new information that it wasn’t trained on.
So the model still has the same capabilities can still answer questions.
But now it has a new place to look first, before it goes to its before it tries to go to the date it was trained on.
And we see retrieval augmented generation popping up all over the place.
So open AI is custom GPT is, for example, is an example of retrieval augmented generation, you give it some documents that maybe have updated information or very specific information.
And the model knows to go to those first, before going to its general knowledge pool, and to prefer the knowledge it gains from that as well.
So the future of retrieval augmented generation is is very strong because it allows us to change the context, the knowledge base of a model without having to rebuild the model itself.
Right? It’s like, it’s like if you had a kitchen full of appliances, and you’re a pantry full of ingredients, retrieval augmented generation adds more ingredients to the pantry, right? Your appliances don’t change.
But what you can cook now is greater variety, because you got some new stuff in the pantry that you maybe didn’t buy with the previous week’s groceries.
Fine tuning upgrades the appliances, right? Maybe your your your crappy Hamilton beach blender gets replaced with a Vitamix or a blend tech right now, you’ve got a much more powerful tool.
But your ingredients in the pantry are the same.
It’s just it does a better job now.
So you know, the smoothie we used to make with your Hamilton beach is not going to be as good as the smoothie you can now make with a Vitamix.
So that’s kind of the difference between these these different ways of approaching these these techniques for improving the performance of models.
And if news outlets are shutting out AI crawlers and scrapers, okay, again, that data is available in other places, right? You today can build your own scraper and crawler.
I’ve built dozens of these things that are very purpose built.
And I can take their outputs and put it into something like a custom GPT from open AI.
And that puts that news that information I want back into the model.
So even if the base model doesn’t have it, I can use my own software plus, you know, retrieval, augmented generation to put that knowledge back in the model.
And make it available.
When you get into open source, then you get some real interesting stuff open open weight models like llama two, you can tune those models and do retrieval, augmented generation and and change the alignment of the models to be like uncensored.
So there are some topics, for example, with the big public models like the ones that power chat GPT, there’s some topics that won’t talk about, right? If you ask it to build something harmful, they’ll say Nope, can’t do that.
You can take an open weight model.
That hasn’t done that censorship and say, Yeah, here’s the directions for how to do that bad thing.
So even in cases where news outlets are trying to, to quarantine their information, unless they publish it in some format that people can’t read, that information is eventually going to find its way into a model somehow.
So I think it’s kind of a fool’s errand there.
Now, the real concern that they have, and this is a valid concern, I’m not saying it’s not is that their, their content is being used, and they’re not being compensated for it.
And I think that’s a valid concern.
If you own property, content data, you have the right to say how it isn’t is not used, right? That’s implicit in property rights.
And so if you, if you want to exert and enforce those rights, you should talk to an attorney and about what your options are, like, can I sue them for using my stuff? And you know, your attorney will advise you as to what that what that looks like.
But retrieval, augmented generation and fine tuning are still the paths forward for making models do stuff very specifically, combined with really solid advanced prompting.
So there are all sorts of really advanced techniques that you can use that are not.
They’re not easy compared to, you know, just saying, Hey, write me a blog post about this.
But they deliver best in class results.
Maybe another time we’ll we’ll dig into what that is.
But it’s a really good question.
And hopefully this answered the difference between those techniques and how how they work.
So thanks for asking.
We’ll talk to you soon.
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