When I tell people what I do, I often get the question: I know I should be using gen AI, but besides [ChatGPT and its ilk] I’m not sure what I should be doing. When I start exploring their needs, I realize that most people have a distorted idea of what generative AI is. In this post I will explain it as simply as I can. Maybe this will give you ideas, or perhaps you will realize that this technology does not apply to your business (at least for now). Let’s go.
The thing that prompted the explosion of AI is the concept of the Transformer. The idea is very simple, pay attention now. As you ingest text, you do not pay attention to one word at a time. You are aware that I asked you to pay attention as you read this, for example. Your mind remembers that I am talking about the Transformer, and each word has a relation to every other word in the context. For example, you know that the explosion I mentioned earlier is not literal, you did not imagine a scene from a Michael Bay movie. Same as when I said the word Transformer, even though Transformers is indeed a Michael Bay movie. Context is vital.
The name of the Transformer should be a hint for what it does. The original motivation was to improve machine translation. If you want to translate a concept from, say, Spanish to English, you do not translate each word independently. Instead you look at a whole phrase Spanish, and come up with another in English that conveys the same meaning. It turns out that humans express meaning online in countless ways besides pure text (images are the obvious example). You don’t even need to switch languages. You can take an idea an expand on it, summarize it, make it more or less formal. And the way this happens is what puts the “generative” in gen AI.
Suppose I start a phrase like with “to whom it may” and ask you to guess what comes next. You would bet the farm on “concern.” That one was easy. If I said “your sister called and” then you have more options. “Said” is a good candidate, “petrichor” is not. But if said “your sister called and said that it’s about to rain. It made me think of the comforting scent of” then petrichor is much more likely. This is what all the chatbots do when you talk to them. They are asked to continue a passage that starts with a prompt: “you are a helpful bot, and here is something the person said.” Then what you said follows, and then something like “This is what you respond to the user.” You could play a social game using this mechanism. To make it fun, you could make a player lose if they mention certain words or topics. Try asking ChatGPT about any topics that OpenAI likes to avoid, and you will experience all the mechanisms that they had to build in order to restrict the model. But this is not inherent to LLMs. You could build a system to talk about the topics you want and avoid others, and they do not need to be the same ones Claude or ChatGPT deal with.
By now I imagine you’re thinking “that’s nice, but what can I do with gen AI? How does it help my business?” This is easy for us, because every day we run into situations in which we think “this interaction would be so much better if only…” Some examples.
Onboarding Buddy
It is your first day at Widgets, Inc. Perhaps you were given some quick orientation, and now you have to start doing useful work. You need to know about some customer requirements. Where are they? You ask a coworker, they tell you to search Notion. It wasn’t there, but you eventually find it somehow. The next time you ask another coworker. After a while you have an idea of where everything is. But what if you could have an omniscient buddy with infinite patience and time for you, so you wouldn’t have to disturb your coworkers? Now we can finally have a useful intranet. A chatbot augmented with retrieval (what is known as RAG, or retrieval-augmented generation) can do this. We have built instances of this, for example with LangChain and custom tools to search gmail, slack channels, Notion, etc. Given an information request, the chatbot decides which resources to explore, and then inserts the relevant facts into the context for response generation. One of our cofounders (Diego Basch) has decades of experience in information retrieval, having sold his SaaS search company to LinkedIn. We can help you assess how to best take advantage of your proprietary information to make it helpful to your employees while at the same time residing in a secure server (for example, using an open-source model like LLaMa 3 with full control of the information flow).
Automated Lead Verifier
A car dealer has ten thousand leads. Each one is the phone number of a person who expressed interest in buying a car. Calling each person individually is expensive. Instead, they can use a voice-powered agent to validate these numbers. The agent calls each person and says “hi, I’m calling from [dealership]. I wanted to check that you are still interested in buying a car, is this correct?” The person doesn’t need to respond yes or no, they can ask follow-up questions such as “wait, what dealership?” or “who is this?” and the agent will be able to engage in an unstructured conversation. If the person confirms interest, it thanks them for their time and tells them that a person will follow up. And of course it can leave voice mail. This can whittle down the leads to a manageable number for the fraction of the cost of a call center.
Fashion Assistant
Imagine you have an event coming up—a wedding, a business meeting, or a casual hangout. You want to look appropriate and stylish, but you’re not quite sure what to wear. You could spend hours scrolling through inspiration boards or online shops, but what if you had a personal fashion advisor who instantly understood your preferences, the event type, and even what’s trending in your social circle?
With generative AI, this is possible. A fashion assistant powered by AI can suggest outfit ideas based on the occasion, current fashion trends, your style history, and even the weather forecast. It can combine these factors with items you already own or suggest pieces from online stores that match your wardrobe, making it easy to find the right look with minimal effort. Imagine this assistant as your style-conscious friend who’s always on call, helping you to dress confidently for any occasion.