5 Generative AI Terms Every Sales Professional Needs to Know

Can you work faster and get better results using generative AI? Absolutely! Our research shows that, globally, 61% of salespeople believe generative AI will help them sell efficiently. And, an impressive 84% of those already using it report that it’s indeed increased sales. 

But if you’ve never used it before, chances are, you probably don’t know what to look for. In this blog, we help you get up to speed with five key generative AI terms every sales pro needs to know. And, you, too can learn ‌how to make the most of generative AI to enhance your sales performance. Let’s dive in.

Exploring Essential Generative AI Terms for Sales Success

1. Generative AI
2. Prompt Engineering
3. Parameters
4. Hallucinations
5. Machine learning bias

1. Generative AI

The first place to start is with the term ‘generative AI’ itself. This is the field of AI that focuses on creating new content based on existing data.  

Generative AI is super handy for sales pros. That’s because sales work typically requires routine administrative tasks and extensive interactions with clients, creating substantial amounts of unstructured data. This data includes text from email threads, audio from phone calls, and video from face-to-face encounters. 

This plays to generative AI’s core strengths, as the technology is built to handle large amounts of unstructured data. That’s why, sometimes, you’ll also hear generative AI being referred to as a large language model (LLM). When your technology can handle all this data and use it to generate sales content like emails or scripts, it saves you a ton of time!  

Our very own Einstein AI is a great example of this in action. Built into the Salesforce platform, Einstein auto-generates personalised emails using CRM data in just a few clicks with our ‘Sales Emails’ feature. And, our Call Summaries feature enables you to swiftly create concise and actionable summaries from sales calls. The upshot? You can be more productive. 

2. Prompt Engineering

As a sales pro, prompt engineering is another term that you definitely need to know about to help you get the most out of generative AI. 

Essentially, it means figuring out how to ask a question (the prompt) to get exactly the answer you need. It’s carefully crafting or choosing the input that you give to a Machine Learning model to get the best possible output.

Sounds simple, right? In practice, it isn’t always that easy. Here are some pointers on how to write a strong prompt for generative AI. 

• Define the task: Clearly state what you want the AI to generate. 
• Provide details: Add details to guide the AI, such as the mood, style or tone of the content it’s generating. You can also be more specific and ask it to include elements like a product or an object. 
• Create boundaries (when necessary): Provide rules that the model must adhere to. For example, you might want the output to be under a certain word count, or you might ask it not to include something.  
• Don’t be afraid to prompt more: The content the AI generates isn’t the finished article. If you don’t like what you see, tell it how to change. “Make this shorter” or “Give me more results” are typical examples of extra prompts. 
• Edit, edit, edit: Even when you’re happy with the generated content, you should always edit it to refine the language, verify the information, add the human touch, and tailor it to your voice and needs.

Of course, these steps aren’t an exact science. But by following them and providing clear, detailed, and precise instructions, you do increase the chances of generative AI providing the content you imagined, making your job that much easier. 

3. Parameters

Now for a slightly more technical generative AI term that you’re going to want to familiarise yourself with.  

Parameters are numeric values that are modified during AI training to minimise the difference between a model’s predictions and the actual outcomes. They help shape the generated content and ensure that it meets specific criteria or requirements. 

When you’ve the right parameters in place, it’s easier for the model to recognise patterns and predict what comes next when it generates content. But beware: setting these parameters is a balancing act. Too few and the AI might not be accurate, but too many and it might be too specialised. 

Luckily, from a sales perspective, you’ll never need deep knowledge about setting parameters. But it’s definitely a good thing to be aware of — just so you’ve a general understanding of how the AI works and what makes the model produce accurate and coherent responses. 

4. Hallucinations

Next up, let’s look at a term that you’re going to want to know — but not because it’s a good thing — hallucinations. 

In the world of generative AI, hallucinations occur when the model analyses the provided input but comes to an incorrect conclusion. This produces new content that doesn’t correspond to either reality or its training data. That’s why we call them hallucinations — because they don’t fit with reality.  

Imagine a model trained on thousands of photos of animals. If you asked it to generate a new image of an “animal” and it produced an image of a giraffe with the trunk of an elephant, that would be considered a hallucination. Because while that “animal” looks fun and could be cool to see, it doesn’t actually exist in real life.  

They say you need to keep your friends close and your enemies closer. A hallucination is an important generative AI term to know. It’s evidence of an undesirable outcome and indicates a problem in the generative model’s outputs. You need to be aware of this in case it happens to you when you’re using a tool!

5. Machine learning bias

And, finally, the fifth term that you absolutely need to know (again … not because it’s a good thing) is machine learning bias. 

This is what happens when a computer learns from a limited or one-sided view of the world and starts making skewed decisions when faced with something new. It could be the effect of a deliberate decision by ‌humans inputting data. But it’s more likely a result of them accidentally incorporating biased data, or algorithms making the wrong assumptions during the learning process. The end result is the same — biased, inaccurate outcomes. 

Obviously, this is something you want to avoid at all costs. Biased outcomes are worse than no outcomes at all, especially as 63% of customers globally are concerned about bias in AI. These biased outcomes not only have big impacts on your business right now, but they can also keep reinforcing those biases in future outputs.

At Salesforce, we go to extreme lengths to make sure our AI has no machine-learning bias. So much so that while others have been rushing into AI over the last few years, we’ve taken the time to ask the hard questions of AI — to make sure that our AI is trusted. 

Talking the talk

If you’re new to generative AI, the terms and jargon associated with it might be a bit of a minefield. But it needn’t be. With this simple guide, you can get to grips with the basic lingo of the technology and how it can help you perform sales tasks — so you can talk the talk and walk the walk.  

But be advised: this really is just a bare-bones list of essentials to help you get started. For a more comprehensive selection of generative AI terms, please visit our generative AI glossary.