Conversational AI vs. generative AI: What's the difference?

What is conversational AI and how does it work?

Conversational AI is trained on data sets with human dialogue to help understand language patterns. It uses natural language processing and machine learning technology to create appropriate responses to inquiries by translating human conversations into languages machines understand. The interactions are like a conversation with back-and-forth communication. This technology is used in applications such as chatbots, messaging apps and virtual assistants. Examples of popular conversational AI applications include Alexa, Google Assistant and Siri.

The knowledge bases where conversational AI applications draw their responses are unique to each company. Business AI software learns from interactions and adds new information to the knowledge database as it consistently trains with each interaction. Humans also update these knowledge bases.

Conversational AI may also use predefined responses – or rule-based systems -- for initial responses.

What is generative AI and how does it work?

Generative AI uses deep learning and neural networks to identify patterns and other structures in its training data. It then generates new content based on predictions from these learned patterns. There are various learning approaches to train generative AI such as supervised learning, which uses human response and feedback to help generate more accurate content. Examples of popular generative AI applications include ChatGPT, Google Gemini and Jasper AI.

Organizations can create foundation models as a base for the AI systems to perform multiple tasks. Foundation models are AI neural networks or machine learning models that have been trained on large quantities of data. They can perform many tasks, such as text translation, content creation and image analysis because of their generality and adaptability. Examples of foundation models include GPT-4 and PaLM 2.

Main differences between conversational AI and generative AI functionality

Conversational AI and generative AI have different goals, applications, use cases, training and outputs. Both technologies have unique capabilities and features and play a big role in the future of AI.

Here is a breakdown of the differences between the two:

• Objectives and goals. Conversational AI focuses on human conversations while generative AI focuses on creating content in various forms.
• Use cases and applications. Conversational AI is used for applications such as customer service, virtual assistants and chatbots. Generative AI can be used to write works of fiction, marketing content and meta descriptions.
• Learning and training data. Conversational AI is trained on large data sets with human input, conversations, user queries and responses. Generative AI is trained on different sets of data to learn patterns to create content with predictive patterns.
• Input and output. Users input information in a conversation when using conversational AI. Conversational AI uses this interaction to create responses. Generative AI uses input and data to generate new content by using learned patterns.

Are conversational AI and generative AI mutually exclusive?

While each technology has its own application and function, they are not mutually exclusive. Consider an application such as ChatGPT -- it's conversational AI because it is a chatbot and also generative AI due to its content creation. While conversational AI is a specific application of generative AI, generative AI encompasses a broader set of tasks beyond conversations such as writing code, drafting articles or creating images.

Editor's note: This article was republished in November 2024 to enhance the reader experience.

Amanda Hetler is a senior editor and writer for WhatIs where she writes technology explainer articles and works with freelancers.