Unlocking Long-Term AI Gains: Building Beyond Prompts for Lasting Success

In today’s Deep Dive journey…

• 🎯 Unlocking Lasting AI Value: Discover the pitfalls of short-term AI solutions and uncover the path to enduring customer engagement.

• 👀 How to build with LLM’s for real, instead of engineering prompts for others to use.

• 🤖From Prompts to Profits: Move beyond the allure of prompts to uncover the real revenue generators in AI building.

• 💰 Building Future-Ready AI: Learn the strategic components that drive long-term AI success, from data organization to secure AI deployment.

• 🦾 Editor’s Recommendation: Top Platforms That You Can Use To Build

Hey Caveminds! Andrew here for today’s deep dive. If it only takes you a month to release a new product, you will only see one month of customer interest.

Wrapping ChatGPT has short-term gains — but how do you produce long-term gains for your customers using an LLM?

Deep Dive of the Week: Show Me The Money! - Where Founders Are Betting On The Future Of AI Building

It all seems so obvious.

Add the word “AI” to your company name, build in a LLM to solve a common user problem, and voila, profit.

Many startups are finding quick solutions that wow us with their unique ways to use Generative AI.

Commonly we see social media videos post “I just made X to Y and it looks so real! (🔥🔥🔥) and this is leading engineers to ask leadership - “Can we do this here?”

But inside multi-level organizations leadership is banning the use of ChatGPT and crushing anyone’s dream of changing business models without first understanding how secure the new solution is — or how it will even save them money.

On a scale of fun to reliable, how should employees and consultants convince leadership that a LLM will have a significant ROI?

(Hint: It has nothing to do with “better prompting”)

Buckle up, and let’s understand together what the difference is between Business AI and Social AI and learn where the real money is going to be made so we can all start focusing on building the future together.

Where We Are Today

Large Language Models have unlocked some incredible power for humans. Tasks that would take months are now taking hours and image/video generation is getting better by the day.

At first, we all tried ChatGPT and couldn’t get the darn thing to output the cool stuff others were doing.

So we started using tools like HyperWrite, Jasper, and copy.ai which didn’t do anything special besides write a better prompt for us (behind the curtain) and then show us the magic trick to make us feel like superhuman 🦸

But Humans get bored with Magic Tricks and the social AI bubble is bursting a bit as users are still trying to find product market fit. Most apps (including Chat.ai) employ the following business model…

The red demonstrates the differentiating factor that a company is providing to the model.

The prompts can be engineered to support many use cases and serve well for Copy.Ai and the like, as Generative AI typically can do the heavy lifting to get the user a respectable answer.

There are two major problems with the longevity of this “application stack”:

1. Privacy - we talked plenty about that here

2. Robustness - will this application work for Client X in Healthcare as well as it does for Client Y in Manufacturing?

To solve for robustness, the problem is not in the AI at all. It is in the parsing and organizing of user data.

For example, if you were the owner of 100 apartment buildings - what software would you choose to remember which tenant recently needed a new lightbulb?

Surely, you can use Dropbox Dash to save all your files, store all your user data, and then simply ask “Give me a list of users that needed a new lightbulb by date” to the clever new Dash Search Bar - but the results would be null. Why?

No human organized the tenants by apartment, updated the new tenants, and then linked the maintenance department to the client list.

AI is unable to form these relationships because the humans that own the apartment buildings have not taught it yet.

Machine Learning Operations and Data Labeling

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