How To: Train Custom Chatbots with Custom Business Data

Imagine the following situation: your customer received an incorrect item in the mail. Ugh. They open up your website and click through to the customer support section.

“Hi, what seems to be the problem?” the plug-and-play chatbot solution you just deployed greets them. “I didn’t receive the correct item,” they type. “Oh no, we’re sorry your item hasn’t arrived yet,” it replies. “Let’s check your tracking number!” They sigh heavily and close the window.

That jarring lack of understanding is a grim reality for many encounters with chatbots, and it means that by the time they’ve found a phone number to speak with a human, your customer is even more frustrated than they were before. But it doesn’t have to be this way.

With the right training, chatbots can be an extremely effective tool for customer support, appointment booking systems, checking inventory, and all sorts of other uses. Without adequate training, they’re more likely to end up on some “epic software fails” compilation!

Below, we’ll cover some of the “must know” information about using your data to train an AI chatbot, some considerations to factor in when choosing a chatbot platform, and what the implementation process might look like as you bring your chatbots online. 

(Next stop, world domination!)

The importance of chatbot training

One of the greatest strengths of many AI chatbots—their breadth of “knowledge”—is also one of their greatest weaknesses: Chatbots trained on large volumes of data often put out generic responses based on an insufficient surface level “understanding” of more specialist topics. (Translation: AI tools aren’t always so good at creating content people actually want to read.)

By providing more specific datasets that you’ve curated for the task(s) at hand, you ensure:

• More accurate responses and fewer hallucinations
• Faster resolutions in fewer steps
• Improved contextual understanding
• Lower likelihood of bots needing to refer queries to human agents*

*With that said, having a human-in-the-loop (HITL) approach remains a good idea.

In the same way that you can use prompt libraries to get useful results from gen AI tools, feeding a chatbot relevant and specific information maximizes your chances of creating a chatbot that will actually work how you want it to. The more diverse you can make your dataset, the more thorough your chatbot’s training will be.

In addition to existing help center articles, you might consider including public developer documentation, YouTube tutorials, samples of your website copywriting, or even transcripts of webinars you’ve previously hosted. Don’t be afraid to think outside the box here.

When it comes to actually training your chatbot, there are different ways you can do this. These vary from the simple—providing question and answer pairs—to the more complex, such as using an embeddings model to break down monolithic information that can be stored in a vector database. The options available to you will vary depending on the platform you’re using.

How to choose a chatbot platform

If you wanted to create a chatbot in days gone by, you might have needed a raft of custom code, on-premise solutions, and meticulously predefined questions and conversational flows. These days, much of that manual input can be subbed out for a dedicated chatbot platform.

A few examples of chatbot platforms include:

• Botpress
• IBM watsonx Assistant
• UChat
• Rasa
• Google Dialogflow
• Amazon Lex
• LivePerson

There’s a range of considerations you’ll want to take into account when choosing a platform, from ease of use and flexibility to compatibility with your data, integration options, and support for programming languages or custom models you’re hoping to use with them. Not to mention language support, cost, and hosted vs. open-source solutions, among other features.

There’s no easy answer to the question “which chatbot platform should I use?” but, with a little research, you’re bound to find some suitable options for your next project. We’ll touch more on technical considerations to be aware of below, but be sure to consider qualitative factors like whether platforms were built primarily with transactional or conversational chatbots in mind.

As companies rush to capitalize on rapid growth in the AI space, including the controversial practice of using AI for creative work, we should expect to see more chatbot companies appear on the market in the weeks and months to come. In other words, if a chatbot platform specializing in your space or industry doesn’t exist right now, just give it time.

The process of training chatbots

What training your chatbot actually looks like will vary considerably depending on the platform you decide to use, but you should expect to see a rough pattern something like this:

1. Define your objectives
   Is this bot intended for customer support? Internal use? Sales automation? Use this information to define user personas and figure out how key interaction flows might look.
   
2. Gather and prepare your data
   Collect all the relevant data you might want, such as FAQs and documentation, so you can organize the data and format it correctly for use with your chosen platform.
   
3. Determine how much customization you need to do
   Most platforms come with “out of the box” models and templates that might be suitable for your intended project. You might, on the other hand, need to do some fine-tuning.
   
4. Input your data
   Within the interface of the platform you’re using, upload and annotate all of the data that you prepared earlier and provide any additional context required.
   
5. Configure and evaluate your chatbot
   Use your chatbot platform to make tweaks, such as fine-tuning the model associated with your chatbot, and set up how your chatbot will work. Then, take it for a spin!

Once you’ve deployed and integrated your bot everywhere you want it—website, apps, customer service platforms, etc.—you can monitor its performance. Armed with user feedback, return to your chosen chatbot platform to patch weak spots and improve response quality. This is, to be clear, very much an iterative process rather than a “set it and forget it” sort of thing.

Final thoughts

As we’ve mentioned above, there are several key considerations to bear in mind when training a chatbot if you want it to perform effectively. And, let’s face it, you probably wouldn’t be reading this if you didn’t. Our top three concerns to take into account are:

1. Volume, specificity, and preparation of relevant data
2. Proper training, likely using a reputable chatbot platform
3. Ongoing maintenance and improvement of your bot

All of which sounds straightforward enough, but training a chatbot using custom data isn’t a process that should be undertaken lightly. It requires a lot of time, extensive prep work, and (ideally) input from someone with technical knowledge about the implementation process. Once all that is behind you, however, a chatbot can be a valuable addition to your arsenal whether you’re deploying it to help with customer service, for internal use, or automating processes. 

As more and more businesses optimize their content with AI and integrate chatbots with their systems, adoption of tech like this may become less about (just) future proofing your business and more about doing enough to keep up with the competition. And, with the right training, your chatbot could help you build a brand that’s smarter and faster than theirs.