Conversational AI vs. Non-Conversational AI:
In today's digital era, Artificial Intelligence (AI) has undeniably become a part of daily life. We interact with AI through a variety of channels.
From virtual assistants in smartphones to customer service chatbots. The experience we get from these interactions is varied.
Sometimes, AI can have a continuous conversation and understand the context amazingly. "AI is good at talking"
But sometimes it can only be done by answering basic questions. "AI asks for word answers"
This distinction is rooted in the different types and architectures of AI, especially between conversational AI agents and non-conversational AI agents.
This article will take readers through the basic differences between the two forms of AI, delving into the key elements that make "AI good at talking" able to create complex and natural interactions.
As well as exploring examples of practical applications. Guidelines for starting development and important considerations to get a comprehensive overview of this technology.
AI is good at talking vs AI asks and answers: What is the difference?
The ability to manipulate conversations is considered an important criterion used to classify these two types of AI:
1. Conversational AI Agent or "Conversational AI":
Imagine AI as a friend to talk to, it's designed to interact with us many times, to talk continuously, to understand what we need a little bit of complexity.
And I can also remember what we talked about.
- Working Principle: It is designed to support continuous multi-turn interaction.
- Bottom Line: There is a state management system that is like a "memory" that allows AI to track and remember important information as well as the context of previous conversations. This makes it "talk about things" and continuously.
- Ability: It can understand the complex goals of users and carry out a multi-step conversation process to achieve them, making the interaction deep and close to human conversation.
2. Non-Conversational AI Agent or "AI Asks Answers":
This one is like a robot answering simple questions. You don't have to remember much. Finish the work every now and then.
Most of them are traditional chatbots that can answer straight questions, but if you ask something complicated or need to think a little analytically, you may not be able to do it.
- Working Principle: Responding to user questions or commands from time to time (single-turn interaction) can be compared to the operation of Automatic "Quiz Kiosk"
- Constraint: In general, there is no or very little ability to remember context from previous conversations. This makes it impossible to link conversations in each round.
- Suitability: Dog for work that requires straightforward answers. Simple tasks that are completed in a single step, such as providing basic information or answering frequently asked questions (FAQs).
While both types of AI are useful in different contexts, "Conversational AI" It demonstrates a higher potential to create a user experience that feels good. It is more like talking to real people.
Key elements that drive "Conversational AI"
Behind the complex conversational capabilities of Conversational AI are several technological elements that work together in a cohesive manner. As follows:
- Prompt Templates: Acting as a "guideline structure" or "chapter" for AI responses, it helps define roles, language patterns, tones, and content scopes.
- Tools and Functions: By connecting to external systems or data sources ("arming" the AI), it is necessary to have clearly descriptive metadata as if it were a "manual" for the AI to use the tool correctly.
- Memory or State Management: It is the key to making AI "remember" and "track". The context of the conversation can be continuous, like a "notebook" or short-term memory. You don't have to start counting all over again.
- It helps AI understand the connections between questions and answers in each round. Reduce the need for users to provide redundant information and create seamless conversations.
- Examples of work:
- First Round: User: "I have two cats named Mochi and Sushi."
- Second Round: User: "Please recommend cat food for sushi. I want it to gain weight."
- AI Response with State Management: The AI will be able to associate that "sushi" is the cat that the user mentioned in the first round, and may reply, "Okay, Mr. Sushi. I don't know what kind of cat it is? This information will help me to recommend the right recipes for weight gain better."
- Large Language Models (LLMs): The "core" technology is like a brain that processes and understands human language deeply. Ability to interpret, generate natural statements, and reason in a hierarchy (Chain of Thought)
The in-depth role of large language models (LLMs)
LLMs are not only a component, but they are also considered the key to enhancing the ability of "AI to talk well" by leaps and bounds. With the following notable features:
- In-depth language understanding: LLMs can understand complex meanings. Connotations, and subtle differences in the language spoken by humans.
- Chain of Thought: When faced with a complex question or problem, LLMs can "plan their response" by breaking the problem down into smaller steps. Find or process the necessary information at each step, and then synthesize it into a complete answer or solution. This is what makes it possible for AI to handle tasks that require analysis or planning.
- Language Creative Ability: In addition to answering questions, LLMs can also create content in various formats, such as writing articles, summarizing, translating, etc.
Examples of Conversational AI Applications in Different Sectors
The potential of "AI Talking" has led to widespread application to improve efficiency and create new experiences in various industries. For example:
- Finance and Banking Sector: The chatbot provides basic financial product advice, answers account questions, alerts you to suspicious transactions, or assists with simple transactions, reducing the workload of agents and providing 24-hour customer service.
- Healthcare Sector: AI system helps screen for initial symptoms (emphasizing that it is not diagnostic), Provide general health information, help with doctor's appointments, or remind you to take medications. Increased convenience and easier access to basic health information
- Semester: AI private tutors that adapt content based on learners' understanding, chatbots that answer common questions about the course, or AI foreign language conversation partners. Helps create a personalized learning environment.
- E-commerce and Services Sector: Smart shopping assistant that recommends products based on preferences, customer service chatbots that manage orders. Tracking the status or processing of returns, the system answers questions about products and services to help increase sales and customer satisfaction.
- Tourism and Hospitality Sector: Chatbots help with trip planning Book flights, accommodation, or recommend tourist attractions. Multilingual Traveler Answers
Case studies both domestically and internationally show that the proper use of Conversational AI It can help reduce operating costs. Increase the productivity of your people, and most importantly, build good relationships with customers through a personalized and memorable experience.
How to get started with Conversational AI development
For organizations or developers interested in creating their own conversational AI. There are currently a variety of tools and alternatives available (as of April 2025):
Low-Code/No-Code Platform:
- Google Dialogflow: Google's popular platform has a Visual Builder that integrates with NLU (Natural Language Understanding) capabilities and is easy to connect to other Google services. Suitable for beginners and not very complicated projects.
- Microsoft Power Virtual Agents (Part of Power Platform): A no-code chatbot builder. Focus on on-premises use and connect well with the Microsoft ecosystem.
Developer Framework:
- Rasa: An open-source framework that provides great flexibility to customize and control every component of AI, ideal for development teams who want to create specialized solutions and control their own data.
- LangChain / LlamaIndex: Frameworks that help build applications powered specifically by LLMs to manage LLM connections to external data sources, memory (state management), and complex agent creation.
- Direct use of LLM APIs :
- OpenAI (GPT series): Provides APIs for accessing powerful language models with a wide range of language capabilities.
- Google (Gemini series): An API for the latest models from Google that are highly capable of both language and multimodal.
- Using the API directly provides maximum flexibility, but requires more development and infrastructure management skills.
The choice of tool depends on the complexity of the project, the skills of the team, the budget, and the level of control required. The important thing is to start by setting clear goals and scope of work.
Usage Considerations and Limitations
Despite the high potential of conversational AI, its practical implementation has some issues to consider and be careful about:
- Accuracy and reliability: LLMs can generate inaccurate or non-existent data (Hallucinations), requiring a process of validating AI-generated data, especially in tasks that require high accuracy, such as providing medical or financial advice.
- Data Privacy and Security: Handling the personal data of users interacting with AI is extremely important.
- Bias: AI may learn and exhibit hidden biases in the data it is training on, which can lead to unfair or inappropriate responses. It is necessary to monitor and reduce bias in the system.
- Complexity of development and maintenance: Building and maintaining a good conversational AI system requires expertise and resources. Dialogue design and continuous improvement.
- Over-dependence: Users should be encouraged to be aware of their limitations and use AI data appropriately.
Recognizing these considerations will help ensure a responsible and sustainable adoption of Conversational AI.
conclusion
Conversational AI Agent It has the ability to interact continuously and more complex than AI that focuses on answering face-to-face questions or "Non-Conversational AI Agent". Obviously, this is the result of the collaboration of key elements such as State Management (memory), Connecting Tools/Functions with Metadata (Guides), Using Prompt Templates (Chapters), and Language Processing Power of LLMs (Machines) with Chain of Thought Capabilities
The application of conversational AI is widely seen in a wide range of industries. It helps to increase efficiency and create a better experience.
However, the development of applications needs to consider the limitations and challenges in terms of accuracy, security, and bias.