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Retrieval vs. Generative Chatbots: Best Choice for Your Business in 2024

Choice between Retrieval or Generative Chatbot

Chatbots are becoming increasingly useful in our daily lives, from virtual voice assistants like Siri, Google Assistant, and Alexa to customer support chatbots on websites.

Chatbots are designed to provide human-like responses to user queries. They can be broadly categorised into two types: retrieval-based chatbots and generative chatbots. This article aims to examine the differences between these two types of chatbots, their advantages, disadvantages, and their practical applications. Let’s start with understanding the basics of retrieval-based chatbots.

What are Retrieval-Based Chatbots?

Retrieval-based chatbots are a type of conversational AI system that operate by matching user inputs to a predefined set of responses stored in a knowledge base. They use natural language processing techniques to understand user intents and queries, and then retrieve the most appropriate pre-written response from their database to provide back to the user.

How do they work?

Retrieval-based chatbots rely on a curated knowledge base containing conversational scripts, information, and responses customised to specific use cases and domains. When a user sends a message, the chatbot analyses the input using techniques like pattern matching, keyword identification, and intent recognition to map the user’s query to the most relevant response in its database. The chatbot then returns this pre-written response to the user.

Examples of Retrieval-Based Chatbots

Mitsuku: This chatbot contains over 300,000 predefined response patterns and a knowledge base of over 3,000 objects. Mitsuku can construct songs and poems based on its knowledge base.
A FAQs ChatBot for Babcock University, Nigeria: This chatbot answers questions that current and future students or the general public may have about the university.

Advantages of Retrieval-Based Chatbots

Predictable: They provide 100% consistent responses as they select from a predetermined set of data and answers.
Less Risky: There is no likelihood of generating inappropriate or nonsensical answers.
Integrated: They can connect to various systems, such as Student Information System(SIS), Customer Relationship Management (CRM), and Learning Management System (LMS), out-of-the-box, enabling them to handle a higher number of queries.
Fixed Cost: There are no variable fees, like tokens, to consider.

Disadvantages of Retrieval-Based Chatbots

Limited Flexibility: They are restricted to their predefined responses and may appear less “intelligent” if they repeatedly provide the same responses to slightly varied questions.
Longer Training Time: They require a significant amount of data, definitions, and intents to answer and provide data, links, or videos.

Now, let’s understand Generative Chatbots

What are Generative Chatbots?

Generative AI chatbots are artificial intelligence-powered and use deep learning and natural language processing (NLP) to generate human-like text responses in natural language during conversations with users. They are designed to understand user input, context, and intent and then generate contextually relevant textual responses.

How do they work?

Generative AI chatbots are trained on vast datasets of text from the internet, books, articles, and other sources. They use natural language processing and deep learning models to process and generate text. When a user sends a message, the chatbot preprocesses and tokenizes the input, breaking it down into smaller units called Tokens. It then uses these tokens to create an initial representation of the user’s message and generates a response by predicting the next words or tokens based on its training data and learned language patterns.

Examples of Generative Chatbots

ChatGPT: Developed by OpenAI, it engages users through natural language conversations and can generate text in various styles.
Gemini (Bard): Built on Google’s Gemini language model, Bard operates as an AI-powered chatbot and it uses Google Search for real time information.

Advantages of Generative Chatbots

Human-like Interactions: They can engage users in natural and interactive conversations, enhancing customer experiences.
Automation: These chatbots can automate routine tasks, freeing up time and resources.
Versatility: With their ability to understand and generate various types of content, they have applications in multiple fields, including customer support, content creation, virtual assistance, education, and healthcare.

Disadvantages of Generative Chatbots

Ethical Concerns: The potential for biassed or harmful responses, uncensored responses, and misuse of technology are significant concerns.
Inaccurate Information: Generative chatbots sometimes provide inaccurate or inappropriate responses due to the biases present in their training data.
Resources: Generative chatbots require significant computational resources and extensive training data to mitigate biases and inaccuracies, which adds up to the cost of development and maintenance.

Comparison and Potential Improvements

Retrieval-based chatbots and generative chatbots are two distinct approaches to building conversational AI systems. While both aim to engage in meaningful interactions with users, they differ in how they generate responses

Challenges with Retrieval-Based Chatbots:

Retrieval-based chatbots rely on a pre-defined set of responses. They use techniques like keyword matching, machine learning, or deep learning to select the most appropriate response from their database. They are often used in closed-domain scenarios and are good at handling a large volume of requests. However, they may appear less flexible and may struggle with generating dynamic responses.

when dealing with complex or ambiguous queries. They may struggle to understand the user’s intent and provide generic or irrelevant responses. Additionally, they are limited by their pre-defined responses, making them less adaptable to changing needs.

Challenges with Generative Chatbots:

Generative chatbots, on the other hand, create original responses by generating new combinations of language. They are trained on vast datasets and use advanced deep learning and natural language processing techniques. These chatbots can engage in more human-like conversations, handle context, and provide contextually relevant responses. However, they require extensive training data and are more complex to develop and optimise.

Generative chatbots, despite their impressive capabilities, have their own set of issues. They may generate inaccurate or misleading responses, known as “hallucinations or confabulations” They can also exhibit biases and perpetuate harmful stereotypes if not carefully trained and monitored, very resource intensive to train on custom data. Furthermore, generative chatbots require significant computational resources and extensive training data, making them more costly to develop and maintain.

How can businesses address the limitations of retrieval-based chatbots‘ inflexibility and generative chatbots’ potential for inaccuracies and biases, ensuring accurate and most importantly training on your own data?

Solution to Addressing Limitations

To address the limitations of both retrieval-based and generative chatbots, businesses can use a hybrid approach called Retrieval-Augmented Generation (RAG). RAG combines the strengths of both retrieval-based and generative chatbots. It enhances the capabilities of generative chatbots by allowing them to access external knowledge bases or databases. By retrieving relevant information from these sources, generative chatbots can provide more accurate, up-to-date, and contextually appropriate responses. RAG improves the quality and relevance of the generated text, making chatbots more reliable and trustworthy. To learn more about RAG Chatbots, you can read (Here)

FAQs

What is the key difference between retrieval-based and generative chatbots?
- Retrieval-based chatbots use pre-written responses from a knowledge base, while generative chatbots generate new responses based on user queries using pre-training, natural language processing, and deep learning.
What are the main advantages of retrieval-based chatbots?
- Predictable responses, less risk of inappropriate answers, the ability to integrate with other systems, and fixed costs.
What are the main disadvantages of retrieval-based chatbots?
- Limited flexibility, longer training times are needed to build the knowledge base.
What are the main advantages of generative chatbots?
- More human-like interactions, automation capabilities, and versatility across different applications.
What are the main disadvantages of generative chatbots?
- There are concerns around ethical issues, the potential for inaccurate information, and the high resource requirements for training.
What is the Retrieval-Augmented Generation (RAG) approach, and how does it address the limitations of the two chatbot types?
- RAG combines retrieval-based and generative techniques to leverage external knowledge sources and improve the accuracy and relevance of generated responses.
How can businesses train generative chatbots on their own data to improve accuracy and reduce biases?
- using the RAG approach to incorporate custom data and knowledge sources into the chatbot’s training and response generation.

Conclusion

When choosing between two types of chatbots—retrieval-based and generative—it’s important to consider their strengths and weaknesses. Retrieval-based chatbots provide consistent answers and can connect with different systems, but they might struggle with flexibility. Generative chatbots can interact more naturally but may sometimes give incorrect information and require lots of data.

To solve these issues, a hybrid approach called Retrieval-Augmented Generation (RAG) combines the best of both approaches, giving better, more accurate responses. While both approaches have their advantages and disadvantages, when combined, they will be far more effective and solve some of the issues of both approaches, ultimately helping businesses improve customer service and efficiency.

Use the RAG Chatbot for improved customer engagement, accuracy, and efficiency!