If you’ve ever wondered why everyone uses generative artificial intelligence on a personal level, but there are so few use cases in the business world, here are three reasons that can explain it:
It’s too generic: Generative AI has learned from millions of data points, but this data is general—collected from thousands of public sources. However, a company makes decisions based on its own business data, which is private. These data have not been learned, so the AI doesn’t provide good answers to questions about each specific business.
It hallucinates: Generative AI produces nonsensical answers from time to time—this is known as “hallucinations.” Without the necessary business context, hallucinations are much more frequent when questions involve business-specific cases.
It’s outdated: Generative AI is not up to date. It has learned from a lot of data, but from the past, since collecting new information is a massive effort. This means it doesn’t take into account information from the last few weeks or months.
How do we solve this?
The most obvious option would be to create a custom model for each business, training the AI with our own data. The problem with this approach is that it is too costly in terms of time and money.
That’s why nowadays I’m seeing more and more solutions that use RAG (Retrieval Augmented Generation), which is a way to personalize the general generative AI model by giving it the right context before it answers our questions.
To do this, we first collect the relevant business documents, index them, and store them as vectors in a database. Then, when asking our questions, we instruct the generative AI to understand what we want, but to answer using only the information from the stored documentation. We could also point directly to the documents at the same time as we ask about them.
With this technique, we completely eliminate hallucinations because we’ll get answers based on our documents. Moreover, the solution will always be up to date, since we keep indexing new documents as they are generated.