According to Gartner, by 2027, Chatbots will become the primary customer service channel. We’re rapidly heading towards a world where AI based Chatbot to answer FAQs will become the norm. This shift isn’t just about keeping up with technology—it’s about revolutionizing how we serve our customers efficiently and cost-effectively.

IBM reports that implementation of Chatbot technology can cut operational costs by up to 30%. Chatbots are your front line in customer service, handling FAQs and repetitive queries—leaving your human team to focus on the trickier, more personalized interactions, making sure every customer feels valued.

In this blog, we will:

  • Understand the difference between Chatbots and AI based Chatbots
  • Learn how to build our own AI based Chatbot to answer FAQs

Chatbots vs AI based Chatbots to answer FAQs – what’s the difference?

Chatbots are interactive software programs designed to automate conversations with current or potential customers, providing both information and assistance.

Chatbots can be of two types: Structured Chatbots and AI based Chatbots.

1. What are Chatbots?

When people use the term “Chatbot,” they normally mean Chatbots that function on structured flows, meaning they follow specific, pre-set rules to interact. They are great for straightforward tasks like filling out forms or providing exact payment details. These Chatbots excel at giving reliable answers, but are limited in their capacity to handle complex questions.

2. What are AI based Chatbots?

AI based Chatbots use the strengths of two technologies—structured flows and Generative AI models. By using this hybrid approach, AI based Chatbots can handle a wide range of questions and even tasks.

The workings behind AI based Chatbots at Tars consist of three prominent models. Let’s explore them with the scenario of a customer who wants to visit a clinic for a checkup.

  1. Q&A Model: This allows the Chatbot to answer FAQs, automate repetitive conversations, and assist in informing customers, e.g. “Can I just walk into the clinic or do I have to book an appointment in advance?” or “Is there a fee to reschedule my appointment?”
  2. Intent Detection Model: Helps the Chatbot understand the intent and context of user queries. For example, if the customer types, “I’ve had a sore throat for almost a week,” the Chatbot will understand that the customer might want a diagnosis of the symptom or to book an appointment with a doctor.
  3. Extraction Model: It converts natural language into structured data, which can be used to facilitate administrative purposes. For example, if the customer says, “I need to know how much a throat swab test will cost at your clinic,” the model identifies and extracts key details like ‘throat swab test’ and ‘cost’ and quickly provides the right information from clinic’s records.

These models ensure that AI based Chatbots are equipped to exceed expectations and assist customers.

Using Generative AI in Chatbots: benefits, concerns, and limitations

Structured Chatbots are generally known for their reliable and predictable nature, although they can be limited and only answer pre-defined questions. In contrast, Generative AI has emerged as a technology that can dynamically answer questions.

But despite its potential, according to a report by BCG of 2,000 global executives, more than 50% still discourage Generative AI adoption. Problems of hallucination, limited traceability, and compromised data privacy are some of the major concerns with this technology.

Aspect Advantages Limitations
Dynamic Responses Generative AI can adapt its responses based on the context and nuances of the conversation, making interactions more fluid and human-like. While AI can adjust its responses dynamically, it may sometimes produce unreliable or contextually inappropriate answers.
Ability to Answer Questions Capable of understanding and responding to a wide range of questions, Generative AI can provide information and solve problems across various domains. Generative AI might fabricate facts or provide misleading information if the training data is flawed or biased.
Impact on User Interaction Enhances customer engagement by providing prompt and relevant responses, thereby improving the overall customer experience. Potential to generate harmful content or responses, which could lead to misinformation or adverse effects on the customer and your business.

Does that mean Generative AI is not fit to automate customer interactions? No!

Platforms like Tars have mitigated the risks of hallucinations and fabricated answers with the approach of RAG + OpenAI. RAG (Retrieval Augmented Generation) is an architectural approach that can improve the efficacy of a large language model. This reduces the possibility of hallucinated or incorrect information in model outputs.

In short, your AI based Chatbot can have the best of both worlds—the reliability and predictability of a structured Chatbot, plus the flexibility and dynamic responses of Generative AI.

Now, let’s get into a step-by-step approach to build your very own AI based Chatbot.

How to create and use an AI based Chatbot to answer FAQs – A detailed approach

Step 1: Planning your AI based Chatbot to answer FAQs

Start by identifying the most common questions your customer support team receives. This could be about your products, services, or general company information. If you have a FAQ section on your website, this is also a good place to start.

Compile these FAQs in a single place, which will serve as the knowledge base of your AI based Chatbot.

Step 2: Building and training your AI based Chatbot

There are two ways to build and train your AI based Chatbot on your knowledge base or training data.

  • Build a training database as a CSV
  • Using advanced AI Chatbot platforms like Tars to automate the training process

Method 1: Build a training database in the traditional method with CSV Files

AI based Chatbots use knowledge bases or datasets to train and answer questions. In the traditional method, we create a training dataset using a CSV.

Here’s how you go about doing this in the traditional method with CSV files:

  • Begin by creating a CSV file with all your FAQs.
  • Upload this file to your Chatbot platform
  • Train your Chatbot on these data points.

To train the bot, you have to manually separate questions and answers in a spreadsheet. However, don’t think that you have to think of all the different ways a question can be asked. You can simply provide 4-5 variations of a single question and specify the answer for them and the Chatbot will be able to figure out the rest.

Here’s what your CSV should look like.

Sample Dataset for training your AI based Chatbot

Next, you have to upload this training data. Don’t forget to publish the Chatbot!

Method 2: Using advanced AI Chatbot platforms like Tars

Platforms like Tars offer tools that automate the training process.

  • Simply upload your FAQ resources
  • Train the Chatbot

To use this method, you have to update the knowledge base of your AI based Chatbot. This can be in the form of website URLs, documents, PDFs, and any kind of written content. You can even use Slack, Google Drive, Notion or Zendesk as your knowledge sources (here’s a quick guide on doing that).

Once you have uploaded the knowledge base, train the AI based Chatbot.

Now, your AI based Chatbot can start answering user questions by referring to the data it has received from the knowledge base. Tars Converse Chatbots understand natural human language and can simulate a human-like conversation with your customers.

You can find a detailed guide on create Tars Converse Chatbots here (previously known as Tars Prime).

Step 3: Implementing the AI based Chatbot to answer FAQs

Integrate the Chatbot into your existing customer service platforms, such as your website or customer relationship management system. Platforms like Tars offer easy integrations with Zendesk, Salesforce, Hubspot, or Zapier—and can easily integrate with your existing business processes using API gambit configuration.

At this stage, ensure your Chatbot adheres to relevant data protection regulations like GDPR or HIPAA. This is crucial for maintaining user trust and legal compliance.

Step 4: Testing, optimizing, and deploying your AI based Chatbot

There are two areas in which you can test and optimize your AI based Chatbot:

  • Whether it is functioning correctly
  • The quality of responses

Platforms such as Tars offer easy testing and optimization of your AI based Chatbot. To test the functionality of your Chatbot, you can go into Debug Mode which helps proofread large/complex conversational flows and automates the entire Chatbot flow.

Secondly, to test the quality of responses, Tars offers AI Self Evaluation, which tests responses based on a given dataset of questions and desired answers.

Once you are satisfied with the functionality and the quality of responses of your AI based Chatbot, you can deploy it on your website.

Step 5: Continuous improvement of your AI based Chatbot to answer FAQs

After deployment, all that remains is an ongoing monitoring and improvement of your AI based Chatbot to continue to wow your customers.

By leveraging Chatbot analysis tools on your Chatbot platform to get data-driven insights, you can ensure that you’re offering the best possible experience to your customers. For example, Tars User Flow Analytics helps you analyze how users interact with your Chatbot to identify bottlenecks or confusion points, and use these insights to improve the conversational flows.

Conclusion: AI based Chatbots are the future

Gartner predicts that by 2027, Chatbots will become the main source of communication for customer service channels. Building your own AI based Chatbot is one of the key methods to stay ahead of the curve—start by defining your requirements and exploring our No-Code Builder today.

At Tars, we’ve worked in the Conversational AI industry for over 7 years. Having seen impressive outcomes with our structured Chatbots, we decided to go one step further and build Tars Converse AI to equip businesses with their own AI Agents.

These custom-made AI Agents take on two main personas: Concierge AI and Advisor AI. They are built to solve specific business problems while enhancing customer journeys from start to end.

Explore use cases of AI Agents across industries: Finance, Government, Healthcare, and Insurance.

For a hands-on demonstration or to discuss your specific needs, schedule a quick demo.