The Perfect Combination: Conversational AI & Generative AI

AI

As AI progresses, understanding the distinctions and the synergy between conversational AI & generative AI is getting extremely important to see the impact of them on the digital arena. While these two fields work together, they have distinct capabilities and functionalities. So in this blog, we’ll clarify the differences and highlight how they collaborate to create a value and transformation for the businesses.

 

Conversational AI

Conversational AI allows machines to communicate with humans more naturally. The interaction between human and the machine is a two-way dialogue, making it crucial for the parties to monitor the conversation flow. Such technology powers tools like chatbots, voicebots, and virtual assistants. 

Conversational AI models are trained on data sets containing human conversations  to understand natural human language patterns. They utilise natural language processing and machine learning technologies to understand the inquiry and create appropriate responses to them, converting human dialogue into machine-readable formats. Conversational AI sources its responses from a database, either by learning from interactions or through human-guided training, known as supervised and unsupervised learning methods.

Generative AI

Generative AI empowers users to produce new content, including text, images, audio, and animations, leveraging machine learning and data. It employs deep learning and neural networks for content generation. Notable implementations are ChatGPT, Google Bard, Bing Chat, and Jasper AI.

Generative AI uses neural networks to detect patterns within its training data, producing new content based on these recognized patterns. ChatGPT, for instance, leverages deep learning and NLP techniques to interpret natural, conversation-like queries. It goes through extensive text data sources, from public records to online articles, and delivers concise but clear summary answers.  

Main differences between conversational AI and generative AI functionality

Although they are both subfields of AI, conversational AI and generative AI have different goals, applications, use cases, capabilities and features. They both play a big role in the future of AI.

Here is are the main difference between the two:

Conversational AI systems typically learn from compact datasets of dialogues to interpret user inputs, analyze them, and produce text or voice replies. Hence, their primary objective is fostering interactive exchanges between humans and machines, with output creation being a secondary outcome.

Generative AI tools, on the other hand, are built for creating original output by learning from data patterns. So unlike conversational AI engines, their primary function is original content generation. So generative AI is a more flexible tool by creating content in different formats, whereas conversational AI tools can only communicate with users.

How do conversational AI and generative AI collaborate?

Although each technology serves its distinct purpose, they are not mutually exclusive, but they empower the overall experience from different aspects. For instance, ChatGPT utilises both conversational and generative AI. While ChatGPT functions as a conversational AI tool by facilitating user interactions, it also exemplifies generative AI with its content generation capabilities.

  • Human-like Responses: Generative AI’s natural language processing is equipped to answer questions in a way that resembles human interaction. Therefore using generative AI, chatbots and virtual assistants can generate more humanlike responses, instead of rigid flows and predefined responses.  These models can produce relevant answers even for unfamiliar queries, thanks to their ability to generalize from learned patterns. Additionally, they adapt over time, offering more fluid and context-aware conversations with users.
  • Optimized Effort: Generative AI doesn’t require high maintenance. It mostly processes and generates data without much intervention. It is not fully autonomous, but because it requires little effort to run, this improves time and resource efficiency. Whereas conversational AI requires regular optimization to both improve its Natural Language Understanding (NLU) as well as adapt to how customers interact with it versus how conversation designers anticipated interactions to flow.
  • System Integration or Channels: It’s not possible to integrate generative AI into all business systems, and with no connection to your channels, it won’t be able to assist customers in relation to your support, products, and services. So in order to provide generative AI to the service of enterprises for various purposes, a platform is required. For instance, when building a virtual assistant, the company needs conversational design, channel integrations, differentiating the responses based on the channel, integrations for company specific responses and prompt engineering for designing the specific responses, in short the company needs a conversational AI platform.

 

Conclusion

Conversational AI and Generative AI complement each other seamlessly. By balancing out each other’s limitations, they combine to create a robust AI solution that businesses can utilise for customer facing virtual assistants, employee support assistants or agent assist tools. 

In hybrid models, the Conversational AI technology keeps the conversation on track and offers company-specific responses, such as opening hours, office locations, transactions, etc. On the other hand, Generative AI algorithms bring creativity, fluidity, and flexibility to the interaction. 

At CBOT we use hybrid models with Conversational AI models and GPT (Generative Pre-trained Transformer) technology, which is a specific model of Generative AI, finding the right combination of the two, based on the use case, the specific needs and the purpose of the project. If you want to learn more about how CBOT combines the power of Conversational AI and Generative AI, please reach out to us.