Why Does Building a Generative AI Chatbot Make Sense?

We used to approach chatbot assistance cautiously, but today the distinction between human and chatbot interaction has been blurred. The introduction of generative AI solutions, and generative AI chatbots in particular, has significantly impacted conversational technologies, prompting businesses to reconsider their operational and customer engagement strategies by building an AI chatbot to hit a home run.

Fast Facts About Generative AI Chatbot Business Initiatives

• By 2025, 30% of outbound messages from large organizations will be automatically generated, up from less than 2% in 2022. (Gartner)
• By 2027, chatbots are expected to be the primary customer service channel for approximately a quarter of companies. (Gartner)
• 64% of businesses believe chatbots will let them provide a better-customized support experience for clients. (Statista)
• Virtual customer assistants reduce call, chat, and email inquiries by 70%. (Gartner)

AI-Based Chatbot Example Rundown

You can explore building an AI chatbot to fit your business needs and operation-oriented queries. Most AI-powered chatbot examples include:

• Customer Engagement Bots. Help answer customers’ queries and requests
• Business Intelligence Bots. Help businesses analyze and structure vast amounts of data
• Sales Bots. Help customers purchase directly or place an order with a business

Generative AI Chatbot Implementation vs. Basic Chatbot Architecture

There is a difference between an AI chatbot and a generative AI chatbot. The distinction lies in the capabilities and underlying technology used in these systems.

Basic Chatbots Powered By AI

An AI chatbot, short for ‘artificial intelligence chatbot’, is a broad term that encompasses rule-based, retrieve, generative AI, and hybrid types. Such AI-based applications can range from rule-based chatbots to more advanced natural language processing (NLP) solutions.

• Rule-based chatbots follow pre-defined rules and patterns to respond to user inputs. They are relatively simple and have limited conversational abilities.
• NLP chatbots use natural language processing techniques to understand and generate human-like responses. These chatbots can be further categorized into generative and retrieval-based chatbots.

Generative AI Chatbot

A generative AI chatbot is a type of chatbot that employs generative models, such as GPT (Generative Pre-trained Transformer) models, to generate human-like text responses. These chatbots do not rely on pre-defined responses or templates. Instead, they generate responses based on patterns and knowledge learned from large datasets during their training.

Not all AI chatbots are generative. Generative AI chatbots are typically more advanced and capable of engaging in natural and dynamic conversations or producing whatever the user asks the bot to do. If we were to make a distinction between generative AI chatbot implementation and a regular chatbot’s behavior and programming, we’d probably highlight the following differences:

A regular chatbot can:

• Behave according to the predetermined rules
• Answer within the framework of expected inquiries which were initially programmed
• Manage repetitive straightforward interactions
• Fail to answer more intricate or nuanced queries

On the other hand, an AI chatbot implementation allows you to create a large language model (LLM) that

• Can process advanced human language thanks to NLP
• Is not restricted to predefined rules and decision trees in order to produce a relevant output
• Has vast understanding based on extensive training
• Handles nuanced scenarios
• Can learn through multiple interactions

What are the Immediate Applications of AI-Based Chatbots?

A generative AI chatbot, like OpenAI’s ChatGPT, are more advanced and capable of generating human-like responses. Their key functions include:

What is Challenging About Building an AI Chatbot?

As you consider building an AI chatbot, it’s vital to assess the roadblocks that seem to recur from project to project. It can be challenging to

• Find the right generative AI chatbot for a specific business need
• Encounter a lack of or insufficient training data to make a non-biased overarching model
• Choose between a custom model and a ready-made foundation model like ChatGPT, which can be re-trained on your data
• Overcome flaws due to the limitations of AI that can’t quite grasp some human-innate manifestations. E.g. portraying human hands in images or creating an intricate plot for a story
• Eliminate hallucinations that are hard to spot unless you’re a subject matter expert on a given topic
• Support multiple languages and understanding inputs that include text, voice, images, or other modalities that add complexity to AI chatbot implementation
• Handle user errors, misunderstandings, and unexpected inputs to provide a smooth user experience. Error recovery strategies need to be well-designed
• Seamlessly integrate a chatbot with the back-end systems in question
• Comply with local regulations across many geographies

Unify Your Generative AI Efforts: Building an AI Chatbot in a Three-Day AI PoC

At Exadel, we adhere to a hands-on approach that involves all possible assessments before any serious decisions are made. Recently, we did a three-day AI PoC that involved building an AI chatbot for a client. We examined ChatGPT-like solutions as well as custom-made ones to see which option can mitigate the challenges and ensure the best generative AI capabilities while accurately answering a series of tricky questions.

Consequently, we evaluated several pre-trained models which included:

You’re welcome to download our full report to learn more about the challenges we’ve encountered, how the models reacted to tricky questions as well as our findings and advice. And should you need help conducting a proof of concept, consider using our expert AI PoC development services.