“Order me dinner.”
You’ve just typed this prompt into your generative AI solution of choice. After all, AI is supposed to make things easier, right?
But now your tool has to make a whole lot of decisions. It needs to figure out:
It uses the information you’ve provided to make its best decision and orders you a hamburger. When it arrives, you realize that you actually wanted pasta. And the delivery shows up an hour later than you were hoping.
So you refine your prompt. “Order me pasta for delivery at 7:30 pm.”
But the bot orders lasagna, when deep down you really wanted spaghetti. You’re still leaving too many decisions up to the AI platform, and you won’t get the meaningful output you’re looking for.
That means you need to refine your prompt even further. “Order me spaghetti from Juliet Italian Kitchen for delivery at 7:30 pm.” Now, all the AI has to figure out is what time the restaurant is open and which website it needs to visit to place the order.
This is what we mean when we say “garbage in, garbage out.” The outputs generative AI provides are only as good as the inputs you feed it. Designing the perfect prompt for AI isn’t rocket science - but it is an art.
Gold in, gold out
While we wouldn’t recommend asking Hapax to order you dinner, you can ask it questions like, “Does this loan application include all of the required information to make an informed decision?” or “Can you create a new procedure for handling high-value wire transfers tailored to our bank?”
Hapax differs from other generative AI solutions in that it can ingest all the information it needs to give you highly tailored and compliant outputs. Bankers can upload any of the following to Hapax:
And with the latest regulations baked into its design, Hapax has all the context it needs to generate helpful and informed outputs. Plus, you only have to upload these materials once. Because Hapax doesn’t just store information, it learns from it.
Let’s continue with our loan application example. Loan admins spend an inordinate amount of time ensuring all the information needed to make an approval decision is present. They need bank statements, property value, any current loan documents or lease agreements, tax returns…the list goes on.
If a banker turns to a generic generative AI solution like ChatGPT or Microsoft Copilot for guidance, those solutions will only be able to provide a general answer based on public information. They’ll comb the internet looking at lending trends and spit out a non-specific response. They might tell our banker, “Based on industry data, people with a credit score above 700 should be approved for financial loans of this amount.”
But frankly, that’s not deep enough.
Bankers need insights like these on a case-by-case basis. They need to know the best way forward based on region-, institution-, and client-specific data in order to provide customers with the best possible service. They need to know if the application falls within their bank’s guidelines; even with the help of ChatGPT, they’ll still need to examine whether the loan aligns with their institution’s processes and whether it includes the right information to make an informed decision.
This is where Hapax comes in.
If our banker were to upload a loan application to Hapax, the platform wouldn’t pull its answers from anywhere on the internet. Instead, Hapax will examine client information, local regulations, and existing loans to gain context. It can look at that loan application and decipher whether the client meets your institution’s criteria based on your lending policies and procedures.
Armed with this context, Hapax has enabled our banker to make the most informed decision possible in a fraction of the time. And when the next loan application comes along, our banker doesn’t have to start at square one. Instead, they’ll get a more thorough output because the platform learns from every interaction.
It’s time to outsmart the future
In keeping with the pasta dinner analogy, the banker eliminates a vast amount of rogue decision-making from generative AI by giving it specific parameters to work within. Providing Hapax with internal policies and other institution-specific data is the equivalent of specifying the dish, restaurant, and time frame in which you want that dinner delivered.
To put it simply, no more hamburgers when you wanted spaghetti.
Are you ready to elevate your AI game? Download The Banker’s Guide to Crafting the Perfect Generative AI Prompt now and start crafting the perfect generative AI prompt!