Answering customers’ frequently asked questions is an important service for businesses to carry out for a number of reasons. Being on hand to support is not only the right thing to do, it also helps to retain a positive sentiment toward your brand, keeping the likelihood of repeat custom high.

However, their frequency means the task of answering them eats into your time as a business, preventing you from working on other tasks or spending more time on complex requests that require more input.

AI chatbots, thankfully, can now automate the answering of FAQs across most communication channels for businesses looking to provide support in a less time-intensive way.

Here, we’ll briefly run through what FAQ chatbots are and show you how to train them so their benefits can be felt immediately.

What is an FAQ chatbot?

An AI-powered FAQ chatbot is a virtual assistant that answers common audience questions by learning information from a pre-existing knowledge base.

For example, if a customer wanted to know how to reset their password, the chatbot can provide an instant answer because it has learnt the process. This prevents the need for a human agent to be involved in something which, while important, is quite routine.

Chatbots began as static tools, which were strictly rule-based, meaning their ability to help with complex requests was limited.

Today, though, they are powered by large language models (LLMs) and have become contextual AI agents which can interpret existing information and improve future answers based on new information they receive from users.

The 3 types of FAQ chatbots

‍Contextual AI Agents

These bots act based on the context they are given by the user and carry out complex tasks such as booking meetings and resetting passwords.

Dynamic LLM-based bots

Bots that have been built with an LLM are great at handling FAQs in a way that feels natural, freeing up teams to focus on other tasks.

Static rule-based boats

These are very basic chatbots which simply find information and repeat it back to the user. They are limited in features but do have some use.

Step-by-step: How to train an FAQ bot

Whichever type of chatbot you decide to use, there will be a process involved to feed it with information and optimise it so that it can hit the ground running and free up your team.

While every business will have particular needs and its set-up may reflect that, there are still some broad steps that every chatbot will go through during set-up.

Step 1: Collect and organise your FAQs

The first step doesn't actually involve the chatbot at all. It's about gathering all the relevant data that you want the chatbot to learn and distribute to users when creating FAQs.

Gather common questions from chat logs, support tickets, and emails and compile them into a document that a bot can interpret. Analyse which questions are most frequent and important, and prioritise those.

Then create clear, concise answers to those questions in a way that is accurate and reflective of your brand, ensuring they are up to date.

Step 2: Train your bot with quality data

Feed the questions and answers into the chatbot, as well as other information from your knowledge base, such as policies, product information, and additional contact details.

Provide phrasing variations and use synonyms. Remember, not every user is going to ask an FAQ in the same way, so train the bot to understand different ways of asking how to reset a password, for instance.

Begin testing with sample questions so get a feel for how the bot reacts to being asked queries. Overall, just ensure you’re prioritising high-quality structured data during this training phase.

Step 3: Use context and continuous learning

Every chatbot improves when it learns from prior interactions, so be sure to continuously optimise them by making them aware of the intent behind questions and the wider context. This can be done with scheduled training using new tickets.

Getting user feedback is crucial at this stage, too.

When someone is done interacting with a chatbot, send a follow-up message asking how satisfied they are with the answer they were given.

Step 4: Balance automation with human escalation

Chatbots are great at frequently asked questions, but not every answer can be automated. There will be times when complex or more sensitive queries arise, and these should almost always be handled by a human agent.

Handing said issues over to human agents prevents customer frustration toward a chatbot that can no longer help. This should also involve training the bot to recognise more urgent queries. Phrases such as “I need help” or “This didn’t answer my question” are signs that the AI needs to step aside.

Best practices for ongoing optimisation

Managing an AI chatbot doesn't end at step four. It should be treated as a live entity which needs regular maintenance and training to ensure it continues to scale with your business and provide the best, most up-to-date answers.

• Regularly review questions that weren’t properly answered by the bot to assess why, so that improvements can be made.
• Keep the content you feed the bot up to date and aligned with product changes so customers always get the right information.
• Performance metrics such as accuracy of answer and customer satisfaction scores should be benchmarked and analysed regularly, as verbal feedback may not always be forthcoming.
• Involve human agents in improving the bot. Their insights will help to provide greater context around more complex answers, allowing the chatbot to take on trickier requests in the future.

Why Mava makes FAQ training simple

Mava is an AI chatbot tool that can be deployed across the popular community channels Slack, Discord, and Telegram. It has a built-in ability to learn from past chat history to improve the answers it gives to FAQs.

Its easy set-up means teams with little to no technical knowledge can set up an in-programme chatbot in just a few hours.

As your company and community grow, so will Mava. Its natural scalability makes it a popular choice for small businesses expecting big growth who want a platform that can handle more and more requests.

With prices starting from $0, businesses can retain a high level of customer support without the need to increase headcount.

Conclusion

FAQ automation saves time and money without sacrificing the quality of the customer’s experience.

This sweet spot can be achieved by following those four easy steps, and with tools such as Mava on hand, automotive FAQs become simple and effective.

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