Understanding the Different Types of Chatbots: A Comprehensive Guide for Businesses

It’s safe to say most online and consumer-facing businesses are either using a chatbot already or plan to do so soon. If this applies to your business, it’s vital to understand the types of chatbots that currently exist and their capabilities before upgrading or adopting this technology.

It’s estimated that nearly three-quarters of businesses have either implemented chatbots or plan to in the near future. The arrival of generative AI and natural language processing (NLP) means that often unpopular and incredibly limited rule-based chatbots are falling by the wayside. Instead, businesses can choose chatty new AI tools to engage their customers but some may find the costs, need for expertise, and risks prohibitive. 

Chatbots are conversational tools that respond to queries or questions. They range from the most basic, which are menu-based and pre-programmed, to AI, voice, and today, generative AI chatbots, which are able to produce entirely new content based on (often) massive repositories of data. 

Menu-Based Chatbots

The most basic type of chatbot is a menu-based or button-based application in which a user chooses a button option from a menu. These bots work on a decision tree basis, so choosing a button prompts either an answer or displays more options until the user is led to an answer.

These bots are simple, easy to set up, and usually inexpensive. They only provide (usually short) predefined responses and don’t have free text fields for users to enter questions. The user experience relies on how the bot is set up and can be a source of frustration if there’s no option or response appropriate to the user query.  

Rule-Based Chatbots

The rule-based chatbot uses a decision tree if/then approach and works like an interactive FAQ. It takes a little more design and is populated with predefined “rules” or question-and-answer combinations. Rule-based chatbots use keyword detection. They are more conversational than menu-based chatbots but are still limited to responding only with the pre-written content with which they are programmed. 

Again, these bots are still relatively inexpensive and available off-the-shelf. They take more time to set up, as taking the time to cover as many “query” eventualities as possible in the bot’s programming will limit user frustrations. However, there will still be frustrations, as when the bot cannot understand a query, it may repeat its request for information or offer irrelevant results.

As users often won’t understand how a rule-based bot is programmed and may even today expect a more fluid, contextual, generative AI response, it’s usually prudent for rule-based bots to seamlessly move users to a human agent to limit their frustrations.  

Conversational AI Chatbots

AI improved chatbots significantly, helping them to engage with users more naturally. The first and most popular conversational AI chatbots, or assistants, are Alexa, Google Assistant, and Siri. 

Conversational AI bots are trained with human dialogue and use natural language understanding (NLU), natural language processing (NLP), and machine learning. Usually, these bots have their own databases or models which are frequently updated as the application learns or when the developers update the systems knowledge. They can also use rule-based systems. With deep learning, a chatbot that’s been used for some time will have consistently improved its responses, developing its learning based on user interactions and becoming much better than when it was first released. 

Conversational AI chatbots with their own independent models or datasets can be expensive to develop, program, and maintain but can also be powerful internally controlled tools. The developing organization’s full control mitigates some of AI’s risks. In contrast, using open-source large language models (LLMs) or third-party closed models removes some of the transparency and control gained by an in-house build. 

Generative AI Chatbots

Generative AI can combine conversational AI technologies with new developments that use neural networks, NLP, and foundation models trained on large quantities of data. These bots are capable of human-like conversations and a certain degree of contextual understanding and can generate entirely new outputs, including content creation. OpenAI’s GPT-4 is a foundational LLM, and it underpins ChatGPT.

Building a chatbot somewhat similar to ChatGPT can cost hundreds of thousands of dollars. OpenAI lost a whopping $540 million in 2022 while developing ChatGPT and in the run-up to its release in November 2022. Analysts in May 2023 estimated that running ChatGPT was likely costing OpenAI around $700,000 per day, given the computing power required. 

Morgan Stanley has built its own chatbot as an internal virtual assistant using GPT-4 technology. In contrast, insurance innovator Lemonade built its insurance chatbot, AI Jim, in-house with some very specific skills, including being able to settle insurance claims within seconds. 

However, there are less expensive ways for organizations to harness generative AI chatbot technology and even use GPT-4 technology by deploying off-the-shelf custom GPTs. OpenAI now offers users the ability to develop custom GPTs using its technology but populated and trained with the user’s own information and preferred settings. CustomGPT.ai offers a business-grade zero-code platform where users can build their own custom GPT chatbots in minutes, again populated with their own business data. CustomGPT.ai uses LLMs and retrieval-augmented generation (RAG) technology so that its chatbots deliver accurate responses based on the user content provided, mitigating the risk of hallucinations and inaccuracies. 

Voice Chatbots

Voice chatbots that use more basic technologies can be limited and result in similar frustrations to those experienced by users of text rule-based bots. However, AI is also evolving voice chatbot functionality using text-to-speech and speech-to-text technologies as well as NLP for more seamless voice conversations and vastly improved responses. ChatGPT began to roll out its voice and image capabilities in September 2023. 

Choosing a Chatbot for Your Business

Whether a company opts for a simple, inexpensive, off-the-shelf chatbot solution or chooses to build an AI chatbot from scratch, in-house, will depend on its budget, specific needs, applicable risk tolerance, internal technical capabilities, and the complexity of the application required. 

A good start in choosing a chatbot for your business is to understand the capabilities and the risks associated with each type of bot before determining the actual value a chatbot or AI chatbot deployment will contribute. 

Generative AI is shifting business preference from rule-based chatbots to conversational commerce. Effective integration is a process that considers four key aspects: business strategy, technology, people and processes, and governance. 

CustomGPT.ai’s inexpensive off-the-shelf zero code chatbot solution uses Advanced large language models (LLMs) with RAG. Developer data solutions company Tonic recently evaluated the performance of applications that use RAG, including OpenAI’s Assistant, CustomGPT.ai, Google’s Vertex Search and Conversation, Amazon Titan, and Cohere. CustomGPT.ai outperformed OpenAI in Tonic’s RAG benchmark. 

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