What is chatbot?

1. Introduction to Chatbots

Chatbots are computer programs designed to simulate human conversation. These digital assistants can engage in text or voice interactions, providing answers to questions, assisting with various tasks, or even holding conversations that mimic human dialogue. Chatbots operate through software that processes natural language, making them versatile tools across industries. They act as a bridge between users and systems, making communication with digital platforms more intuitive and efficient.

In recent years, the rise of artificial intelligence (AI) has positioned chatbots as key tools in modern business applications. From customer support to e-commerce and healthcare, chatbots have become integral to enhancing user experience and operational efficiency. They provide 24/7 support, reduce waiting times, and offer personalized assistance, which has led to their adoption across industries. For instance, in retail, chatbots guide customers through product selections, while in healthcare, they assist with scheduling and preliminary patient screening. As AI technology continues to evolve, chatbots are gaining advanced capabilities, becoming essential in our digital interactions.

The concept of chatbots isn’t new. The journey began in 1966 with ELIZA, one of the first chatbots, developed by Joseph Weizenbaum. ELIZA simulated a psychotherapist's role, using basic pattern matching to respond to users. Although primitive by today’s standards, ELIZA demonstrated the potential for human-computer interaction, sparking interest in developing more sophisticated conversational agents. This historical reference underscores that while chatbots have come a long way, the interest in human-like conversation with machines has been a continuous journey.

2. The Evolution of Chatbots

Early Beginnings

The initial development of chatbots was grounded in simple rule-based systems. ELIZA, created in the 1960s, relied on pre-set patterns to respond to specific keywords. Its approach to conversation was limited, often repeating phrases or asking simple questions without real comprehension. Despite these limitations, ELIZA’s design proved influential, inspiring further exploration into conversational software. Another notable example is PARRY, developed in 1972, which aimed to simulate the behavior of a person with paranoid schizophrenia. Although PARRY used a more complex rule set than ELIZA, it was still fundamentally limited by the rigidity of its rule-based system.

Modern Advancements

Advancements in AI have transformed chatbots, with natural language processing (NLP) and machine learning (ML) enabling more dynamic and adaptable conversations. Today’s AI-powered chatbots, such as Apple’s Siri, Amazon’s Alexa, and Google Assistant, go beyond simple rule-based interactions. These virtual assistants use large language models (LLMs) and NLP to interpret context and respond conversationally. Unlike early chatbots that followed strict, predefined responses, modern chatbots learn from data, enabling them to answer a wider range of questions and even carry on complex dialogues. By processing vast amounts of text data, they adapt to user inputs more naturally, making interactions smoother and more helpful.

Key Milestones

The development of chatbots gained significant traction after 2016, driven by advancements in AI and the growing demand for automation in customer service. Studies in chatbot research show a spike in publications and interest, reflecting their increased application across industries. For instance, the adoption of chatbots in customer service allows businesses to handle high volumes of inquiries efficiently, significantly reducing response times. This growth has paved the way for even more sophisticated conversational agents, such as chatbots integrated with large language models that provide nuanced responses in a variety of contexts.

3. Types of Chatbots

Rules-Based Chatbots

Rules-based chatbots operate by following a fixed set of predefined instructions. They work best for straightforward tasks, such as answering frequently asked questions, providing business hours, or assisting with simple troubleshooting. These chatbots depend on a structured decision tree to respond appropriately; users choose from options or type specific keywords that trigger scripted replies. Due to their simplicity, rules-based chatbots are cost-effective and reliable for routine queries but lack the flexibility to handle complex or open-ended conversations. They are widely used in FAQ sections of websites, where consistency and adherence to brand messaging are prioritized.

AI-Powered Chatbots

AI-powered chatbots represent the next generation of conversational agents. By incorporating NLP and ML, these bots can interpret the context of a conversation and generate responses that mimic human understanding. Unlike their rules-based counterparts, AI-powered chatbots don’t rely solely on scripted responses. They learn from interactions, adapting to new inputs over time, and can respond to more nuanced questions. This adaptability makes them ideal for customer service, where queries are varied, and conversational flow needs to be flexible. IBM’s Watson Assistant and Google’s Dialogflow are examples of AI-powered platforms, showcasing how AI enables chatbots to understand intent, recognize patterns, and engage more naturally with users.

Hybrid Chatbots

Hybrid chatbots combine rules-based and AI-powered functionalities to provide a balance between structure and adaptability. They can handle simple questions through predefined rules, while more complex queries are managed with AI capabilities. This approach is beneficial in customer service, where a chatbot might handle routine inquiries independently but escalate more complicated issues to human agents or an AI module. Hybrid chatbots are versatile, capable of offering both efficiency and depth in responses. For example, a retail chatbot might provide standard responses for common product inquiries but use AI to recommend personalized product suggestions based on browsing history.

Examples

Different chatbot platforms offer various capabilities based on their underlying technology. IBM’s Watson Assistant, an AI-powered chatbot, uses advanced NLP and ML to provide tailored responses, making it suitable for diverse applications in healthcare, finance, and customer service. On the other hand, Google’s Dialogflow is designed for businesses to build conversational interfaces that work across multiple platforms, including voice assistants. These examples illustrate how rules-based and AI-powered technologies are applied across platforms, enabling businesses to choose solutions that fit their specific needs for automation and customer engagement.

4. How Chatbots Work

Overview of Core Functions

At their core, chatbots are designed to interact with users through text or voice, interpret their inputs, and generate appropriate responses. This process starts when a user sends a message or question to the chatbot. The chatbot then parses this input, analyzing it to determine what the user is asking. Based on the interpreted intent, the chatbot either provides a response or takes an action, such as retrieving information from a database or performing a simple task.

This interaction relies heavily on language processing and pattern recognition to ensure that the chatbot understands the user correctly and provides accurate responses. Whether the chatbot is answering a straightforward question, handling a customer service request, or guiding users through a multi-step process, it follows similar principles: analyze the input, identify the intent, and generate a response.

Main Components

1. Natural Language Understanding (NLU): This component allows the chatbot to make sense of human language. NLU processes the user's message to understand the meaning and context. It breaks down the input into different parts, identifies keywords, and recognizes the overall intent of the message.

2. Intent Detection: Intent detection is essential for chatbots to understand the purpose of the user's message. For example, if a customer asks, "What’s the status of my order?", the chatbot must detect that the intent is to inquire about an order's status. Intent detection enables chatbots to route the message to the correct response or action.

3. Entity Recognition: Entity recognition is the process of identifying specific pieces of information within the message that add context. For instance, in "What’s the status of my order #12345?", "order #12345" is an entity. Recognizing entities helps chatbots provide relevant responses by pinpointing details like order numbers, dates, or locations.

4. Response Generation: After understanding the user’s intent and identifying any entities, the chatbot moves to response generation. This can be a simple scripted message, a dynamically generated response, or an action that fetches information from a connected database. The response generation ensures that the reply is meaningful and appropriate based on the context.

Example

Consider a customer who asks, “Can you check the status of order #56789?” Here’s how each component plays a role:

1. NLU interprets the sentence, recognizing it as a request related to order status.
2. Intent Detection identifies the main intent: the user wants information about an order.
3. Entity Recognition detects "order #56789" as the specific order in question.
4. Response Generation queries the database for the order status and replies with, “Your order #56789 is currently being processed and will be shipped soon.”

Through these steps, the chatbot offers a seamless and efficient response, providing the user with precisely the information they requested.

5. Key Technologies Behind Chatbots

Natural Language Processing (NLP)

Natural Language Processing (NLP) is the technology that enables chatbots to interpret human language. NLP encompasses multiple subcomponents:

• Natural Language Understanding (NLU): This subcomponent focuses on understanding the meaning behind user inputs, as discussed earlier.
• Natural Language Generation (NLG): NLG allows chatbots to create human-like responses. After NLU identifies the intent and entities, NLG formulates a coherent and contextually appropriate reply. This enhances the user experience by making the responses sound more natural.

NLP serves as the foundation for chatbots, enabling them to engage in meaningful conversations by understanding and generating language in a way that feels intuitive to users.

Machine Learning (ML) and Large Language Models (LLMs)

Machine learning (ML) plays a critical role in making chatbots smarter and more adaptable. By analyzing vast datasets, ML enables chatbots to improve their performance over time. For example, a chatbot trained with ML can refine its responses based on historical interactions, allowing it to handle new questions more accurately.

Large Language Models (LLMs) like GPT-3 and BERT have propelled chatbot technology forward. These models, trained on enormous datasets, enable chatbots to generate responses that are highly context-aware. LLMs help chatbots understand nuances, slang, and varied sentence structures, making conversations feel more fluid and less robotic. LLMs have also advanced the chatbot’s ability to handle open-ended queries that go beyond simple yes-or-no answers.

Additional Technologies

Some chatbots employ Retrieval Augmented Generation (RAG), a technique that improves information retrieval by leveraging external databases or documents. For instance, if a chatbot is integrated with a company’s internal knowledge base, RAG allows it to access that data in real-time to answer complex questions. This approach is especially beneficial for customer service chatbots, as it enhances their ability to provide accurate and up-to-date information.

6. Benefits of Using Chatbots

24/7 Availability

One of the most significant advantages of chatbots is their ability to provide round-the-clock support. Unlike human agents, chatbots do not require rest and can operate continuously. This allows companies to deliver customer service anytime, meeting customer needs even outside regular business hours. For example, an e-commerce website can use chatbots to answer product-related questions, assist with orders, or handle returns at any time, ensuring customers always receive support.

Cost-Effectiveness

Chatbots are highly cost-effective as they can automate repetitive tasks, reducing the need for human intervention in simple inquiries. By handling routine queries, chatbots allow human agents to focus on more complex issues, which optimizes resources and lowers operational costs. For example, Salesforce data indicates that using chatbots can significantly reduce response times for common customer service inquiries, leading to greater efficiency and customer satisfaction.

Personalization

With access to customer data, chatbots can provide a personalized experience. They can remember user preferences, purchase history, and past interactions, allowing them to tailor responses based on this information. For instance, a chatbot might suggest products a customer has previously viewed or recommend services based on past inquiries. This level of personalization helps businesses enhance user engagement and build stronger customer relationships, making interactions more relevant and impactful.

Example

Salesforce reports that chatbots have contributed to higher customer satisfaction in customer service settings by improving response times and providing consistent support. With automated and personalized responses, chatbots have become a valuable asset for businesses aiming to deliver a smooth and effective customer experience.

7. Common Use Cases of Chatbots

Customer Service

One of the most prevalent uses for chatbots is in customer service, where they help companies respond quickly to frequently asked questions (FAQs), handle common inquiries, and resolve basic issues without needing a human agent. Chatbots can guide customers through simple troubleshooting steps, provide account information, and even process returns or refunds in some cases. By automating these repetitive tasks, chatbots free up human agents to handle more complex requests, significantly reducing response times and improving customer satisfaction. For example, many call centers utilize virtual agents to assist with high volumes of incoming requests, as demonstrated by Google Cloud’s virtual agents, which help reduce wait times and streamline customer service operations.

E-commerce

In the e-commerce industry, chatbots enhance the shopping experience by offering personalized product recommendations, answering questions about product availability, and helping customers track their orders. For instance, a chatbot can suggest items based on the user’s browsing history or recent purchases, improving the chances of a sale. Additionally, chatbots are often available 24/7, making it easy for customers to get help at any time, which enhances convenience and encourages repeat purchases. Some e-commerce sites even deploy chatbots to manage abandoned carts by reminding customers about their unfinished orders and offering incentives to complete the purchase.

Healthcare

In healthcare, chatbots have become valuable tools for patient triage, appointment scheduling, and information provision. A healthcare chatbot can guide patients through preliminary screening questions to assess the urgency of their symptoms, helping direct them to the appropriate care. They are also used to schedule appointments, reducing the workload on administrative staff and minimizing wait times for patients. Additionally, healthcare chatbots can answer common questions about treatments, medications, or medical conditions, providing valuable support without overburdening healthcare providers. Google Cloud, for instance, has implemented virtual agents in healthcare settings, helping manage patient inquiries and streamline administrative processes in clinics and hospitals.

Examples

Various industries have adopted chatbot technology to streamline processes and enhance user experience. Google Cloud’s virtual agents in call centers are a prominent example, where chatbots help manage a large volume of incoming calls and inquiries, allowing human agents to focus on more complex cases. This approach has proven beneficial in both customer service and healthcare, demonstrating how chatbots can adapt to industry-specific requirements to provide efficient support.

8. Differences Between Chatbots and AI Agents

Definitions and Capabilities

While chatbots and AI agents are similar in their ability to interact with users, there are notable differences in their definitions and capabilities. Chatbots are generally designed to handle specific tasks, such as answering FAQs or guiding users through straightforward processes. They rely on predefined scripts or basic AI to respond to queries, making them well-suited for structured, customer-facing roles.

AI agents, on the other hand, are more advanced and capable of handling complex reasoning and data-driven decision-making. They are equipped with machine learning algorithms that allow them to adapt and improve over time. AI agents often perform more sophisticated tasks, such as analyzing data patterns, prioritizing tasks, or generating insights, and they are more commonly used in internal, employee-facing scenarios where deeper analysis and adaptability are required.

Use Cases

Chatbots are ideal for customer-facing roles where structured interactions are needed. For example, a rule-based chatbot can assist customers with questions about account information or product details on an e-commerce website. In contrast, AI agents excel in employee-facing roles that require more complex processing. For instance, an AI agent might support a business’s data analysis team by summarizing trends, identifying high-priority items, and even suggesting actions based on historical data patterns. These agents can significantly enhance productivity in departments like finance, marketing, or human resources, where data-driven decisions are essential.

Example

Salesforce’s Einstein AI Copilot illustrates the advanced capabilities of an AI agent compared to a chatbot. Unlike rule-based chatbots, Einstein AI Copilot can assist employees by analyzing data, prioritizing tasks, and providing deeper insights. This allows organizations to use AI agents for more complex workflows and data analysis, making them valuable for internal operations and decision support in ways that simpler chatbots cannot match.

9. Chatbot Platforms and Tools

Top Platforms

Several popular platforms enable businesses to develop and deploy chatbots tailored to their specific needs:

• Google Dialogflow: Known for its powerful natural language processing capabilities, Dialogflow allows developers to build conversational interfaces that work across multiple platforms, including web, mobile, and voice assistants.
• IBM Watson Assistant: This platform offers robust AI-powered analytics and customization options, making it suitable for businesses requiring advanced conversational capabilities and deeper customer insights.
• Amazon Lex: Integrating seamlessly with other AWS services, Amazon Lex is ideal for e-commerce and retail applications. It provides tools for creating chatbots with voice and text capabilities, allowing users to interact naturally across different channels.
• Rasa: An open-source platform, Rasa provides full flexibility for developers to customize chatbots according to their needs. It is especially popular among developers looking for more control over the chatbot’s behavior and integration with back-end systems.

Features Comparison

• Google Dialogflow excels in language understanding, making it effective for chatbots that require multi-language support and high accuracy in intent detection.
• IBM Watson Assistant stands out with its analytical features and AI-driven insights, which help businesses understand customer needs more deeply and personalize interactions.
• Amazon Lex offers easy integration with AWS services, making it a top choice for businesses already using Amazon’s ecosystem, especially those requiring voice interactions.
• Rasa is unique as an open-source solution, allowing companies to build highly customized chatbots without vendor lock-in, making it ideal for enterprises with specific, unique requirements.

Example

A case study that highlights the use of Amazon Lex is in e-commerce, where it has been successfully deployed to handle a range of customer interactions, from answering product questions to managing returns. By integrating with other AWS services, Amazon Lex enables a smooth flow of information between different systems, helping businesses improve customer experience and streamline operations.

10. Challenges and Limitations of Chatbots

Limited Understanding

One significant limitation of chatbots is their restricted ability to understand complex or nuanced queries. Chatbots, particularly rule-based ones, often struggle to interpret variations in phrasing or unexpected questions. Even with Natural Language Processing (NLP) advancements, chatbots can misinterpret user intent, especially if the question doesn’t match the patterns they were trained on. AI-powered chatbots fare better but still face challenges in fully grasping the subtleties of human conversation. For instance, if a user asks a vague or multi-part question, the chatbot may only provide a partial answer or fail to comprehend the context altogether. Salesforce notes that chatbots often falter when dealing with open-ended questions, as they lack the depth of understanding required to handle such queries without specific programming.

Human-Like Interaction

Another challenge is achieving a natural, human-like conversation flow. While NLP has improved dramatically, most chatbots still fall short of mimicking genuine human interaction. Complex human language involves emotions, colloquialisms, and nuanced meanings that can be difficult for chatbots to process accurately. For example, sarcasm or idiomatic expressions may lead to incorrect interpretations, as chatbots are not yet capable of fully understanding the emotional or cultural context behind words. This limitation affects user experience, as conversations with chatbots can sometimes feel mechanical or scripted, lacking the empathy and adaptability that human agents bring to interactions.

Maintenance and Upgrades

To remain effective, chatbots require frequent updates and maintenance. As businesses evolve and customer needs shift, chatbots need to adapt by integrating new data and improving their language models. For AI-driven chatbots, maintaining relevance involves continuous machine learning (ML) model training to handle emerging language patterns and terms. This upkeep can be resource-intensive, requiring regular data collection, processing, and model fine-tuning to prevent performance degradation over time. Businesses using chatbots must plan for these ongoing updates to ensure the chatbot remains a valuable tool for customers and accurately reflects current knowledge.

Example

Salesforce highlights that while chatbots can effectively handle basic customer queries, they often encounter difficulties with open-ended or intricate questions. This limitation underscores the need for human intervention in more complex cases or a hand-off system where the chatbot seamlessly transfers the conversation to a human agent when it reaches the boundaries of its capabilities.

11. Security and Ethical Considerations

Data Privacy

With chatbots gathering substantial user data, privacy concerns have become prominent. Chatbots frequently collect information like names, contact details, and query history to provide personalized responses. However, this data collection must align with data protection laws, such as the GDPR in Europe or the CCPA in California, which set strict guidelines on how personal data should be handled and stored. Businesses must ensure that their chatbots are compliant with these regulations to protect user privacy. Any mishandling of data or non-compliance can lead to legal repercussions and erode customer trust.

Bias in Responses

Bias is another ethical consideration that affects chatbot interactions. Since chatbots learn from large datasets, they can inadvertently adopt biases present in the data. These biases may reflect stereotypes or exhibit prejudiced language if the chatbot has been exposed to unfiltered content. The risk of biased responses is particularly concerning in customer service and healthcare, where neutrality and sensitivity are crucial. Developers must be aware of these risks and strive to minimize bias by carefully selecting training data and applying bias-detection algorithms.

Transparency

Transparency is essential for maintaining user trust in chatbot interactions. Users should be informed that they are conversing with a chatbot rather than a human, as this clarity helps manage expectations and enhances the interaction experience. Transparency in chatbot deployment also involves explaining how user data is collected and utilized. Google Cloud’s approach emphasizes ethical AI practices, ensuring that users understand they are interacting with an AI-powered system and that their data is handled responsibly.

Example

Google Cloud has implemented ethical guidelines in its AI chatbot development, focusing on transparency and data protection to maintain user trust. By following such practices, companies can address ethical concerns and foster a positive relationship with their customers.

12. Future Trends in Chatbot Technology

Advancements in Generative AI

Generative AI, powered by large language models (LLMs) like GPT-3 and GPT-4, is revolutionizing chatbot technology by enabling more sophisticated and nuanced conversations. These models allow chatbots to handle a wider range of questions and generate more contextually relevant responses. Generative AI also improves the chatbot’s ability to respond to open-ended queries, making interactions more flexible and dynamic. With continuous advancements, generative AI is expected to further enhance chatbots, enabling them to provide detailed answers that closely mirror human conversation patterns.

Increased Use of AI Agents

AI agents are advanced conversational systems that go beyond standard chatbots. Unlike traditional chatbots, which are typically limited to specific tasks, AI agents integrate deeply into workflows, assisting with complex operations such as prioritizing tasks or generating content. AI agents have become particularly popular for employee-facing scenarios, where they assist with various work processes. This trend indicates that businesses will increasingly adopt AI agents for internal operations, allowing them to optimize workflows and increase productivity.

Voice-Activated and Multimodal Chatbots

Voice-activated and multimodal chatbots represent another significant trend in chatbot technology. Voice-enabled chatbots, such as those used in smart assistants, are becoming increasingly popular due to their convenience and accessibility. Additionally, multimodal chatbots incorporate visual and text-based elements, enhancing user experience by presenting information in multiple formats. For instance, Google’s Vertex AI leverages multimodal capabilities, allowing developers to build chatbots that can interact via text, voice, and even images. This versatility enables chatbots to cater to diverse user preferences and provides a more immersive interaction experience.

Example

Google’s Vertex AI platform showcases the future potential of multimodal chatbots, enabling developers to create chatbots that seamlessly integrate text, voice, and visual elements. This multimodal capability allows chatbots to provide more intuitive interactions, giving users a richer and more adaptable experience in various contexts.

13. How to Implement a Chatbot for Your Business

Setting Goals and Use Cases

The first step in implementing a chatbot is to define clear goals and use cases. Consider what you want the chatbot to accomplish: is it for customer service, sales, or internal support? Establishing a specific purpose will help shape the chatbot's design and functionality. For example, a customer service chatbot might be geared toward answering frequently asked questions, while a sales-focused bot could assist users in making purchase decisions. Identifying use cases also involves understanding the types of interactions the chatbot will handle. Common use cases include answering FAQs, providing product information, or guiding users through processes like booking appointments or placing orders.

Choosing the Right Platform

Selecting the right platform is crucial to ensuring that the chatbot meets your business’s needs. Different chatbot platforms offer unique features, so it’s important to choose one that aligns with your objectives. For instance, Google Dialogflow is popular for its powerful Natural Language Processing (NLP) capabilities, making it ideal for complex interactions. IBM Watson Assistant is known for its AI-powered analytics, which can help improve customer insights. For e-commerce, Amazon Lex integrates smoothly with Amazon Web Services (AWS), enabling enhanced support for retail-based chatbots. Factors to consider when choosing a platform include ease of use, scalability, customization options, and integration capabilities with your existing systems.

Implementation Steps

1. Designing Conversations: Start by mapping out potential user interactions. Create conversation flows that anticipate different paths users might take, such as asking basic questions or needing help with complex issues. Designing these flows with a focus on user intent will make interactions smoother and more effective.

2. Training Models: For AI-powered chatbots, the next step is to train the underlying models. This involves feeding the chatbot data on common customer queries and responses, which allows it to recognize patterns and provide accurate answers. Continuous training is necessary to improve the chatbot’s performance over time, especially as customer needs evolve.

3. Testing: Before launching, it’s essential to test the chatbot thoroughly. Use sample queries to see how the chatbot handles different scenarios. Testing allows you to identify any gaps in understanding or response quality, ensuring that the chatbot is ready to deliver a seamless experience.

4. Deployment: Once the chatbot is tested and refined, it’s time to deploy it. Decide where users will access the chatbot, such as on your website, mobile app, or messaging platform. Some businesses opt for multi-channel deployment to reach customers on various platforms.

5. Monitoring and Improvement: Post-deployment, monitoring the chatbot’s interactions and performance is essential. Analytics tools can provide insights into how well the chatbot is meeting customer needs. Use this data to make ongoing adjustments, retrain the model as necessary, and improve the chatbot’s capabilities.

Example

Oracle provides a strong example of a successful chatbot implementation. Their chatbot is used to streamline customer service for enterprises by automating tasks such as handling routine inquiries and guiding users through common processes. By training their chatbot on industry-specific data and integrating it with Oracle’s CRM system, the company has achieved more efficient customer interactions and improved service response times. This case study illustrates how a well-implemented chatbot can enhance customer experience and operational efficiency simultaneously.

14. The Future of Chatbots

Recap Key Points

Chatbots have transformed how businesses interact with customers by providing quick and convenient support. They automate repetitive tasks, offer 24/7 availability, and improve customer engagement with personalized responses. From customer service and sales to healthcare and e-commerce, chatbots are now integral to many industries. AI-driven technologies like NLP and machine learning have also significantly enhanced chatbot functionality, enabling more natural and context-aware interactions.

Final Thoughts

As AI technology continues to advance, chatbots are poised to become even more powerful. With ongoing developments in generative AI and large language models, chatbots are becoming better equipped to handle complex inquiries and mimic human conversation more closely. They are expected to play a critical role in improving user experience, particularly as businesses look for ways to deliver efficient and personalized support.

Actionable Takeaway

For businesses considering chatbot adoption, a phased approach is recommended. Start small by deploying a chatbot with a focused purpose, such as handling FAQs or supporting a single department. Monitor its performance, make improvements, and gradually expand its capabilities as you gain insights from user interactions. By starting with a limited scope, you can effectively measure the chatbot’s impact and scale it up strategically to maximize its benefits for your business.

References:

• AWS | What is a Chatbot?
• Google Cloud | AI Chatbot
• IBM | What is a Chatbot?
• NCBI | Conversational Agents in Healthcare
• Oracle | What is a Chatbot?
• Salesforce | AI Agent vs. Chatbot
• Salesforce | Customer Service Chatbot
• Stanford Teaching Commons | Defining AI and Chatbots

Please Note: Content may be periodically updated. For the most current and accurate information, consult official sources or industry experts.