You Ask, I Answer: How To Vet AI Vendors?

In today’s episode, I share the top questions marketers should ask AI vendors to avoid getting duped. I explain what to look for in their architecture and abstraction capabilities. Asking the right technical questions helps reveal who’s the real deal. Tune in to get my insider tips on vetting vendors to make informed AI purchasing decisions!

You Ask, I Answer: How To Vet AI Vendors?

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In today’s episode, Mark asks, you discuss the importance of marketers asking the right questions to vendors, what are the top questions you believe marketers should be posing to AI vendors to ensure they’re making informed decisions? Okay, this is a really good question because a lot of the time marketers don’t necessarily know what to ask vendors, but they know that they’re well, they’re concerned that a vendor is going to try and pull a fast one, right? sell you something that isn’t really theirs, it’s vaporware, or there are less ethical vendors are hoping you just don’t ask tough questions.

So I tend to ask very technical questions because I want to know the architecture of what’s underneath under the hood, right? I want to know what’s happening on the inside.

And in a lot of cases with artificial intelligence, especially you don’t need to give away if you’re a vendor, you don’t need to give away the secret sauce, right? But you can tell someone what the architecture is just like, you know, if you go to a can of soda, you can see what the ingredients are, you don’t get the recipe, you don’t know how much of each ingredient there is, or there’s a secret process, but you get the ingredients.

So if you open up a can of Coca Cola, you’re not getting Coke’s secret recipe, but you are being told here’s the things that are in this thing that can that make it work or not work.

When it comes to AI, for example, when I’m looking at a company that offers generative AI capabilities, like language generation, I will ask that company, what is your foundation model? Right? And less skilled sales folks will say, Oh, what’s a custom proprietary model that we developed in house and stuff like, yeah, okay.

What’s the foundation model that you tuned on? Because if you know the generative AI space reasonably well, you know that there’s about five companies on the planet that can put together really good, true foundation models.

Now, granted is more than but basically, you need a company that has roomfuls of servers and roomfuls of GPUs to build a foundation model and you need months of time to build one of these things.

Most, most startups, you know, most marketing vendors, they’re not going to have the hundreds of millions of dollars in cash, the people, the server rooms, etc.

To make a true foundation model and we’re talking about models like GPT for which powers chat GPT and GPT for V, Claude to llama to etc.

etc.

The, the big names when it comes to foundation models, these models are huge, they’re huge, they’re complex.

And there are not that many companies can make a true foundation model.

Now, yes, you can build a small foundation model on your laptop, right? There’s actually tutorials online, if you want to build the equivalent of like a GPT to you can do that, and it will take you a really long time.

So when you look at a marketing vendor, a company in the marketing space, they are almost certainly using someone else’s model as their foundation, and then they have custom tuning to that model.

Now, they may have a private fine tuned model, like you can make inside of open AI, they may have an API, they’re just calling an API to talk to somebody else’s model, they may have what’s called prompt efficient fine tunes, which includes things like Laura’s low rank adapters that essentially are like plugins to a foundation model.

So you have a foundation model like llama to and you make a plugin called, you know, the Trust Insights plugin, it’s got all of our blog content in there, it’s got all of our email, newsletters and things.

And it’s been trained on us and you plug it into the foundation model.

And that then in turn, gives you the ability to specialize, or behind the scenes, these companies may have a vector database where a client’s text goes so that the again, the language model knows what things to say.

And you might even not have one model, you might have several, you might have an ensemble of models.

But again, a vendor that doesn’t have anything to hide can explain this right again, there’s no secret sauce being given away.

You might say, yeah, we have an ensemble of models, you know, three of which are based on the llama to family, one of which is one of open AI is models and we counterbalance across all four models.

Great.

That is useful architecture that tells me, you know what you’re talking about, that you that we know what’s behind the scenes was under the hood is the real deal.

But you haven’t given me any of the secrets or you haven’t told me exactly how your model works.

You haven’t told me, you know what your prompting looks like, what your data storage looks like.

And those are all parts that in the final application make a big difference with the user experience and so on and so forth.

But the truthfulness and willingness of a vendor to answer that question about the foundation model tells me a lot about the vendor, right? A vendor who is cagey or deflecting, that’s a vendor I don’t trust, because they should know what’s under the hood.

They should, they should be able to say Yeah, we are we use this or that, right? It’s kind of like go to a restaurant and asking who their ingredient supplier is or is there gluten in this food? And we’re not asking for the recipe.

We’re asking for the chef to come out and explain step by step exactly how it’s made.

We’re just asking, Hey, what’s in this thing so that we know whether or not it’s safe to eat? Especially when it comes to generative AI, the second question I asked is about abstraction.

So abstraction means you have the ability to take a model and swap it out with a different model, right? vendors who tie their entire business to a model they picked at a specific period in time, they put themselves and their customers at risk, at risk of being underperforming being out of date, new models and particularly new foundation models come out all the time.

If you were building your company two years ago and the state of the art at the time was opening eyes GPT three, and you just built everything around that you hard coded in and you’re really proud of this application.

Well, since then, GPT three has been superseded by GPT 3.5 GPT 3.5 turbo GPT 3.5 turbo 16k GPT four GPT four V and you’re now like five generations behind the the current up to date foundation models.

Right? If you have done a good job of building an abstraction layer into your software, then as circumstances change, you can say to the customer, Hey, do you want to use GPT four V for this? Do you want to use Claude to for this? Do you want to use llama to for this? That abstraction if you thought through in your product means giving customers choice.

And those choices can sometimes be very beneficial for customers, your customer who gets a lot of choice in the models they’re allowed to pick, they can, they can choose a model maybe on cost GPT four V is a costly model to run llama to is a relatively inexpensive model to run and depending on the task and maybe your software if it’s very clever can even recommend Hey, for this task, it looks like, you know, llama to is the best choice for you.

That’s those are the questions that I ask AI vendors when I’m having a conversation with them to build the ability to just say like, Hey, what’s in the box? Explain your architecture explain your decisions and the choices you’ve made.

So that I understand how far along you are in your own AI journey.

And I can make good purchasing decisions based on the way you’ve got your system set up.

And so that those are my questions.

Those are my answers to those questions.

So what I look for and you know, there may be other considerations.

There may be considerations like price or data privacy and stuff.

So this is by no means an exhaustive list of the questions to ask.

These are just the ones that I look for.

Because it helps me judge very quickly whether or not someone is the real deal.

So really good question.

Thanks for asking.

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You Ask, I Answer: How To Vet AI Vendors?

Machine-Generated Transcript

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