What Is Generative AI? A Simple Guide with Everyday Business Examples

You ask the software to create an introductory note for your customer in less than a minute. A couple of seconds later, you receive the first draft. After requesting a simpler language, you receive a new version. It is one of the many examples of generative AI – software creates a piece of content based on your request rather than providing a predetermined message stored in its memory.

It may also be known as Gen AI. Even though the name might seem complex, the concept is quite easy to understand. It is capable of creating text, images, and other forms of content. In addition, it is capable of making errors. A business application requires correct facts, a small task, and people who can check the results.

This guide describes the key concepts in plain language. Think of a repair shop that needs assistance with customer communications. We'll use this scenario to describe requests, models, drafts, reviews, and the first project. These are planning examples, not anecdotes about a specific Digitonix customer. You don’t need to know how to code to use this guide.

What generative AI creates

Generative is related to the creation of something. A Gen AI tool can generate a response, a summary, an image, or other types of content, based on the tool. The AWS overview of generative AI provides more context about the general concept. In business, the output must have a specific purpose, for example, helping an employee create a useful draft message.

It does not have to produce a fully completed piece to be useful. This could mean condensing a long piece or even suggesting an improved heading. The results could be reviewed and improved by the people involved. Begin by thinking about what work the content is supposed to support. A paragraph which sounds good and does not answer any actual questions is not going to do much for the team.

How it differs from a fixed rule

The fixed rule uses a known step. For instance, once a booking has been confirmed, use a saved reminder template. A Gen AI tool would be able to come up with varied wording based on the information received. This could be useful in certain kinds of work. It could also mean that the results will be less predictable.

Choose the easier alternative whenever you can. The shop will require a form of tangible confirmation about an appointment schedule. The shop may also use Gen AI in converting the notes taken by the staff members into a readable explanation. These are all technologies that can work hand in hand. Avoid replacing a tried and tested procedure with the generated text whenever the procedure works perfectly fine.

What a model is

A model is the AI part responsible for generating a response. For text, the model uses patterns learned during training to create a reply. Think about it like a tool for working with languages. Do not treat this part as a person who knows everything about your shop.

Also noteworthy is the application tied to the model. It will define what data will be analyzed, what person can pose a question, and what will be the location of the response. Two applications can work with one and the same model, but in different ways. The firm should consider the whole process and should not simply check the name of the model or its response in a demo presentation.

What a prompt is

The prompt is your request to the tool. The general prompt will give a general response. You should not say, “please write something good.” You should describe the task which has to be accomplished. For example, you should ask for a reminder according to the booked appointment.

Keep the request honest about any missing information. If there is no confirmation of when the collection time was, do not tell the machine to fabricate that information. It needs to omit it or seek that piece of information. Even a better request will help improve a draft, but it will not ensure its accuracy. It is up to the team to check important pieces of information before making the draft public.

Use a draft as a draft

The draft is just that: an unfinished piece of work that is not a final agreement. The repair technician can check out the message, edit it and then send it. These choices need to be available on the screen. It is not necessary to send a generated message just because text is entered in the box.

Give the user enough context to review the message. The booking confirmation along with the draft message could be provided in this case. The employee shouldn't have to go through many screens to find the information about only one date. Clarify what the artificial intelligence did. The message was prepared and not yet completed by the person who has to approve it.

Why a clear answer can be wrong

It is possible to produce smooth text without proper facts. It would provide the missing fact with what looks as a valid statement. For example, it might invent a warranty that the shop does not offer. Normally, this type of information provided by the AI is called hallucinations. Just think of them as false information that looks valid.

Check a fact using its valid source. A scheduling system can check your appointment. The current policy would check a service rule. These facts cannot be verified just because of the wording provided by the tool. If a fact is missing, the tool should explain that gap instead of inventing it. Rather than providing a customer with false info, ask a useful question.

Choose a small first task

Think about a single task which can be described and verified. The task will not necessarily mean that the AI gets the power to manage the entire repair shop. This will make the process easier and help the team to see how the result relates to the actual environment.

Make a list of all the things that will go into the first release. Point out all the facts that have been used, the person who has reviewed this and the action taken. Also, point out all the things which are not going to be included in the plan. Creating a reminder does not mean that the visit date needs to be changed or a refund given out.

Protect the information you share

Provide only the information necessary for doing the task at hand. Reminders may require the first name and a specified date. There is no need to include all the notes from the customer's account. Passwords and similar confidential information should not be provided in normal requests. You should be aware of the provider and the settings of the app before sharing any private information.

But, the access restrictions do not depend on whether there is AI involved in the process or not. The employee has to have access only to those records which he is supposed to access, even if it involves an AI chatbot. It is very important to identify who can access specific data. Any generated content or logs should be included in the review.

Plan for waiting and failure

AI request could take some time and would sometimes fail to execute. The system would provide the current status of the request and what should be done next. The worker should know if the request was processed, if some drafts were saved, and if anything was sent. A success message should not be shown for the customer's activity that was not acknowledged yet.

Another alternative solution should be provided if it's required. The repair technician could employ the useful template if the AI application fails to work properly. This should be made easily available and accessible to the worker. It should not involve the need for multiple clicks leading to the creation of multiple requests and sending them. Request references could help understand the issue.

Test with everyday examples

Create an example set of small cases based on the real case. Include a normal case, booking without a date, customer name change, and something that the system would reject. Define what a good response looks like in your opinion. Check the information, wording, and context of the response. An appropriate demo is just an example of it.

Ask employees to use the tool without holding their hand and see whether they are capable of using the tool to draft and review the document properly. List the issues and the people responsible for solving them. Run the test again after the critical changes have been made, as models, requests, and source documents can influence the results, so the first test will not be the last forever.

Count the whole cost

The price of using the service is higher than just the AI request. Storage, application support, testing, and reviewing the answers can add up to the price. The low-priced draft that takes longer to fix won’t be helpful. Compare the entire task before and after the change was done. Make a reasonable sample and see what types of work require human attention.

Do not make a broad statement about cost savings or work reduction by AI. Consider what work became easier and what was added. The company needs someone to take care of rules, updates, and issues after the launch. A sensible budget and support plan would be able to outline such obligations in simple terms to ensure everyone knows what they are maintaining.

How Digitonix can help

Gen AI solutions and software development services of Digitonix could be an appropriate context to discuss a small Gen AI project. Specify a task, safe input examples, and an example of output that would be useful. Ensure that you ask for the review process, access, testing, and the scope at the start that would be clear for the team. Ask what the system can actually check before relying on its answers.

For example, a project would use Python to process the data and Node.js to combine the parts of the application. You would first list the team members and describe the task through the Digitonix contact form.

Frequently asked questions

Does Gen AI understand our business automatically?

It may not know all of your current facts and rules. The app needs to provide the right allowed facts and check important results. A model can create a useful draft that still has wrong details. Choose a trusted source for each fact and decide what a person must review.

Is a chatbot the same as generative AI?

Chatbot represents a means of having a textual dialog. It may employ static rules, Gen AI, or a combination of them. Gen AI is the content-creating technology, rather than the chat screen itself. Do not question its interface but its characteristics, employed facts, and handling of unknown information.

Can we use AI for every customer message?

First, consider the bounded task and validation tests. There are messages which need certain wording or a decision made by the person. Some messages can be enhanced with the help of the review of the draft. Find out what usage is possible for the system and check it with some examples. Do not let a generated message to make promises or do actions beyond your company’s limits.

How do we know whether the feature helped?

Compare the tasks, not just the amount of AI requests. Compare how much time it saves you, factual corrections, message reviews, and tasks which have become productive because of it. Consider the feedback of the users. A good AI generator does not always produce more drafts than the other one; it depends on how much time is spent on fixing them.

What is a good first Gen AI project?

Pick an activity with clear inputs and expected output results. An assistant may have too many uses, and it will be more complicated to check. Fallbacks and scope should also be defined in advance to help you see where there is room for improvement. Fallback is the alternative method of getting the work done if the AI cannot do that.