Generative AI Chatbots vs. Conventional Chatbots: A Comprehensive Comparison

June 7th, 2024

In recent years, the development of generative AI (artificial intelligence) technology has greatly advanced the functionality and performance of chatbots. In addition to conventional rule-based chatbots, generative AI chatbots with natural language processing capabilities have emerged, bringing great potential for realizing customer support and operational efficiency improvements for businesses.

This article will explain in detail the differences between chatbots and generative AI chatbots, introduce the benefits of implementing generative AI chatbots and keys to success in an easy-to-understand manner. It’s packed with tips for companies to effectively utilize generative AI chatbots, so feel free to use it as a reference.

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Generative AI Chatbots vs. Conventional Chatbots: The Ultimate Showdown

1. Overview of Chatbots, AI, and Generative AI

1.1. What are Chatbots?

Chatbots are systems that respond to user questions and requests through text or voice-based conversations. They can be integrated into websites, apps, messaging tools, etc., to automatically handle user inquiries.

The main features of chatbots are:

+ Available 24/7, 365 days a year

+ Can handle multiple users simultaneously

+ Capable of quickly responding to FAQs (Frequently Asked Questions)

+ Can perform simple tasks in place of human operators

In practice, many companies are utilizing chatbots in the customer support field. For example, major telecom company SoftBank operates a chatbot called “Oshiete SoftBank” (Tell Me SoftBank) to assist with contract procedures and explain rate plans.


The Importance of Implementing Chatbots

1.2. What is AI?

AI stands for Artificial Intelligence. It refers to computer systems and software that mimic human intellectual capabilities. AI includes technologies such as machine learning, deep learning, natural language processing, image recognition, and speech recognition.

AI can discover patterns from given data and make inferences and judgments based on those patterns. In other words, a major characteristic is that it has the ability to “learn” like humans. Unlike conventional programming, AI can acquire skills from experience and improve its performance.

Specific examples of AI applications include:

+ Development of self-driving cars

+ Support for medical diagnosis

+ Automation of financial transactions

+ Building chatbots using natural language processing

+ Image and speech recognition

AI is being utilized in various fields, and its importance is expected to increase further in the future. Many conventional chatbots do not have AI technology installed and could only respond according to preset fixed sentences. However, with the evolution of AI’s natural language processing technology, smoother and more natural communication has become possible.

1.3. What is Generative AI?

AI can find patterns in given data and make inferences and judgments using technologies such as machine learning.

On the other hand, generative AI refers to products that are particularly adept at natural language generation (NLG) technology among AI technologies. By utilizing NLG technology, it can engage in natural real-time conversations with humans. By training machine learning models on various dialogue data, it can accurately understand user statements and generate appropriate responses.

In this way, generative AI is being applied to business and has become a powerful tool to effectively support companies’ customer acquisition. The following article introduces 8 ways generative AI can support your business: “8 Ways Generative AI Improves Business Performance”

Differences between AI and Generative AI

In other words, AI broadly refers to the entire technological field that mimics human intelligence, while generative AI refers to products specialized in natural language processing, particularly natural language generation, within that field. A major characteristic of generative AI is that it can achieve more natural conversations.

2. Types of Chatbots

The recent trend is that chatbots are becoming essential tools for websites. This section explains two types of chatbots.

Chatbots can be broadly classified into two types:

image 2

Two Types of Chatbots

2.1. [Chatbot] (Non-Generative AI Chatbot)

2.1.1. What is a Chatbot (Non-Generative AI Chatbot)?

A non-generative AI chatbot refers to a conventional general chatbot. It’s a system that outputs responses based on predetermined rules and patterns. It does not incorporate AI’s natural language processing technology.

In non-generative AI chatbots, administrators pre-register numerous question patterns and answer patterns in a database. Then, it calculates the matching accuracy between the question text input by the user and the registered question patterns, and returns the answer linked to the question pattern with the highest similarity.

2.1.2. Benefits of Chatbots

The benefits of implementing non-generative AI chatbots include the following two points:

Low-cost implementation: Non-generative AI chatbots have a relatively simple structure, allowing for reduced implementation costs. The initial cost is significantly cheaper compared to generative AI chatbots (Gen AI Chatbots).

Can be built and operated in-house: It’s possible to build and operate chatbots in-house without specialized knowledge. You can create a database of questions and answers while utilizing your company’s know-how and FAQ data.

2.1.3. Drawbacks of Chatbots

On the other hand, non-generative AI chatbots have the following drawbacks:

Limited response range: Even if you increase the amount of answer data, it’s currently difficult to handle patterns other than those registered. Depending on the user’s question, it may not be able to respond appropriately.

Difficulty in receiving long-term support: Many non-generative AI chatbots are one-time sale tools, making it difficult to receive operational support after implementation. The need for regular maintenance is also a challenge.

Inefficient data registration work: There’s a disadvantage in that question and answer patterns need to be registered one by one manually, making scaling difficult.

Next, we will explain the features of generative AI chatbots equipped with the latest generative AI technology and specific product examples. We will explain the strengths of generative AI conversation bots that can achieve more advanced dialogues in an easy-to-understand manner, comparing them with non-generative types.

2.2. [Generative AI Chatbot] – Generative AI chatbot

2.2.1. What is a Generative AI Chatbot?

A generative AI chatbot refers to a chatbot that utilizes AI’s natural language generation (NLG) technology. By training machine learning models on a large amount of past dialogue data, it can understand the intent of user statements and automatically generate natural and appropriate responses.

2.2.2. Benefits of Generative AI Chatbots

Implementing generative AI conversation bots can provide the following benefits:

Natural conversations possible: With the excellent language processing capabilities unique to artificial intelligence, it can understand user statements in detail and achieve smooth and seamless conversations with humans. Responses are not robotic and do not stress users.

Can handle a wide range of questions: As it learns from past dialogue data, it can appropriately handle questions beyond set patterns. The range of answers to questions greatly expands.

Furthermore, generative AI conversation bots constantly evolve their response capabilities through learning. They analyze user dialogue histories and continue self-learning to achieve smoother communication. In this way, AI chatbots provide users with a stress-free and highly convenient support experience.

2.2.3. Drawbacks of Generative AI Chatbots

However, generative AI chatbots have drawbacks. The drawbacks of implementing artificial intelligence chat include:

High initial implementation cost: Building generative AI models requires collecting a large amount of dialogue data and training the model. It requires specialized technology and resources, potentially resulting in high implementation costs.

Ongoing operational costs: Regular retraining with new data is essential to maintain and improve model performance. There’s a drawback of ongoing operational costs. In many cases, dedicated engineers are needed, increasing operational costs.

Need for some level of checking system: To prevent errors, it’s necessary to establish a system to pre-check and correct generated response content.

Generative AI Chatbots vs. Conventional Chatbots: The Ultimate Showdown

3. Preparation for Implementing Generative AI Chatbots

When implementing Generative AI Chatbots, there are concerns about whether it will work well. Human responses involve many complex processes, and it’s extremely difficult to reproduce all of these with chatbots. Individual circumstances and consideration for human relationships require human-specific thoughtfulness. If preparation is insufficient at the time of implementation, it often fails to meet user expectations. Therefore, to smoothly implement chatbots, companies need to make the following preparations.

3.1. Sufficient data collection and preparation

The accuracy of Gen AI Chatbots largely depends on the quality and quantity of training data. Without sufficient data that appropriately reflects company-specific terms and contexts, it cannot respond as expected. It’s important to collect a wide range of data such as industry terms, FAQs, product information, past inquiry histories, and properly process them as training datasets.

3.2. Smooth integration with existing systems

To smoothly operate generative AI chatbots, integration with existing systems (CRM, databases, etc.) is essential. If system integration is insufficient, efficient business flows cannot be realized. It’s necessary to reliably link AI conversation bots with existing systems using APIs and other means. An environment where data can be smoothly transferred between systems needs to be built.

3.3. Continuous training and monitoring is essential

To succeed with AI conversation bots, thorough preparation in advance and continuous improvement after implementation are indispensable. Therefore, it’s necessary to build a process that can constantly monitor the operational status of generative AI chatbots and regularly evaluate them. A system to retrain data based on feedback is also important.

4. Introduction to Miichisoft’s dxGAI Chatbot: Let Us Help You!

4.1. What is dxGAI Chatbot?

dxGAI Chatbot is a high-performance solution developed by Miichisoft that incorporates cutting-edge technologies such as generative AI and Retrieval Augmented Generation (RAG).

*Retrieval Augmented Generation (RAG) can generate detailed and accurate responses by retrieving relevant information from large unstructured data and passing that information to generative models. RAG is a combination of information retrieval (Retrieval) and text generation models (Generation). In other words, it fills the gaps in how large language models (LLMs) operate. Much higher response accuracy can be expected compared to conventional AI chatbots.

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Miichisoft’s dxGAI Chatbot

4.2. Features of dxGAI Chatbot

The features of Miichisoft’s dxGAI Chatbot are as follows:

+ UI customization possibility: The UI of dxGAI can be freely customized to match the company’s brand image. Various elements such as logos, fonts, and colors can be adjusted.

+ Ease of implementation: dxGAI Chatbot is a cloud-based solution and is very easy to implement. No special infrastructure preparation is required, and operations can start in a short period.

+ Integration with various tools/platforms: dxGAI Chatbot can be integrated with various tools (Salesforce, ZenDesk, Microsoft Teams, LINE, Slack, etc.) and platforms (iOS, Android, etc.) as well as websites.

+ Detailed analysis function: dxGAI Chatbot is equipped with advanced analysis functions to analyze usage status and response performance. This is useful for identifying improvement points and calculating ROI. The advanced analysis functions of dxGAI Chatbot organize data and reports on interactions between chatbots and customers, which is beneficial for improving the effectiveness of chatbots.

+ Support for over 80 languages: dxGAI supports over 80 languages, sufficiently meeting the needs of global companies.

4.3. Value Provided by dxGAI Chatbot

So, what value can Miichisoft’s dxGAI Chatbot provide to your business?

–  dxGAI Chatbot creates accurate and reliable responses

+ Utilizing RAG technology, dxGAI accurately extracts relevant information from large amounts of data and generates high-quality responses. It can provide reliable information.

+ dxGAI can have advanced knowledge specific to companies and industries by utilizing various external data sources (technical documents, industry data, etc.) and integrating with internal company data (FAQs, product information, knowledge bases, etc.).

+ Furthermore, through continuous automatic learning, dxGAI continuously learns through interactions with users and improves its performance. Regular retraining is also possible, allowing it to always provide the latest knowledge. As a result, it can provide the most accurate and reliable responses to customer needs.

– dxGAI Chatbot can support lead generation

+ It collects data on potential customers and provides personalized purchasing experiences based on the “customer journey map.”

– dxGAI Chatbot ensures data privacy and security

+ dxGAI maximally respects the confidentiality of company data and implements industry-leading security measures. It’s a solution that can be implemented with peace of mind.

5. Conclusion

The major difference between chatbots and generative AI chatbots lies in the presence or absence of natural language processing capabilities. Conventional rule-based chatbots could only respond with preset fixed phrases.

On the other hand, generative AI chatbots can understand natural language to some extent and generate responses according to the situation. However, there are challenges as well. A large amount of data and learning is essential to improve the quality of responses. There’s also the possibility of bias in AI responses, making monitoring indispensable.

At Miichisoft, we have developed the dxGAI Chatbot solution based on RAG technology and generative AI technology. This AI solution is used not only in customer service but also in internal automated FAQs to support company training activities and optimize internal communications.

We understand that many companies aim to implement AI to expand revenue, but it can be very difficult to implement AI from start to finish on your own. That’s where Miichisoft’s experts come in, walking alongside you to build AI models tailored to your company’s goals, supporting productivity improvement, cost reduction, and strengthening competitiveness. Leave the implementation to Miichisoft!

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