Benefits of Training Generative AI Solutions on Your Own Data

Wondering what are the benefits of integrating your data into your Generative AI project? In this clip from my Assessing Data Readiness for Generative AI course on Pluralsight you will see the benefits of integrating your data in your Generative AI deployment.

Full Assessing Data Readiness for Generative AI Course

Video Summary

Here are the key points from the video:

• Generative AI models thrive on large amounts of data, often sourced from the internet. The quality of the AI’s output depends heavily on the quality of the data it is trained on.
• Training AI on your company’s data can provide more accurate and relevant results compared to using only public data. This is because your internal data is more trusted and specific to your needs.
• Practical examples:
  • Troubleshooting: AI trained on your data can offer more precise solutions by accessing past help desk tickets and internal knowledge.
  • Presentations: AI can create comprehensive presentations by combining knowledge from your documents and databases.
  • Email Replies: AI can draft personalized email replies by leveraging customer information, past communications, and order history.
• Integrating your data with AI solutions can make employees more productive by reducing the time spent on crafting prompts and improving the quality of answers.
• Before integrating your data with AI, you need to plan for security and compliance, integrations, and cleaning up old and irrelevant data.

For more information, read the transcript blog below, or watch the video above!

Video Transcript

Hello and welcome to this “Assessing Data Readiness for Generative AI” course. My name is Vlad Catrinescu, and I’ll be your instructor for this course. I’m a Microsoft MVP from Montreal, Canada, and you can find me on X at @vladcatrinescu or follow my blog at vladtalks.tech.

Let’s start by learning about generative AI and your data, and the relationship between generative AI solutions and your data. I want to start by saying that generative AI solutions love data. This is because the large language models used by generative AI solutions are trained on large amounts of data, most of the time public data available on the internet. One thing to be aware of is that the quality of the output is directly dependent on the data that the model is trained on. So, if the model is trained on bad data, the results will also be bad.

But now, where do we find good-quality data? The most trusted and highest quality data is the data that we have in our own company. But what if we could train large language models on our own data? What kind of benefits would that bring? Well, let’s take a look at some examples.

Let’s say that you want to use generative AI to troubleshoot a problem on one of your own products with a customer. A generic generative AI tool only trained on the web would use the data from the web and give you some generic advice based on public data, while a generative AI tool that is trained on your data could look at all of your past help desk tickets and internal knowledge base, in addition to web data, to provide a more complete answer.

Another common scenario we see is creating a presentation about a product or a project. A generic AI tool can only use data from the prompt you give it, so your users will need to craft huge prompts or upload documents as part of their prompts in order to tell the tool about the product that is referenced. While a generative AI tool trained on your data can combine all the knowledge from your documents, databases, and other data sources that it has access to to create that presentation for you.

A third and final example can be as simple as creating an email reply to a customer. A tool that is trained on public data can only create a reply based on the information in the prompt. So, in the prompt, the user must include everything from the goal of the reply to the tone to use, any past conversations, and even the relationship and history between the customer and the user. If you had a generative AI tool trained on the data, your users could simply ask for a reply and what the reply should be about, and the tool will already know the customer information, past emails, and even the order history of that customer from your CRM, as well as all past communications.

So, if we recap, some benefits of integrating your data with your generative AI solution are that employees will be more productive because they will spend less time crafting prompts and more time on work. It will also increase the quality of answers as your data is the most accurate and trustworthy data that you can find. Finally, your users will get more personalized answers as the generative AI tool can now personalize answers based on the roles, access, and past interactions for specific users.

But before we’re able to integrate our data, there are three different tasks that we need to plan. First, security and compliance. After that, integrations. And finally, cleaning up old and irrelevant data. And of course, we will cover all of those in this course.