Chatbot

A chatbot is software that conducts conversations with people, typically through text-based interfaces like websites, messaging apps, or mobile applications.

Chatbots range from simple to sophisticated:

Simple chatbots:

• Follow scripted conversations
• Use keyword matching
• Handle specific, predictable queries
• Example: "Say 'refund' to check refund status"

AI-powered chatbots:

• Understand natural language
• Handle varied phrasings
• Learn from interactions
• Generate human-like responses
• Example: "I see you're asking about a refund. Let me check your order status..."

Modern AI chatbots powered by LLMs have dramatically improved capabilities, enabling natural conversations that feel less like talking to a machine.

Rule-based chatbots Follow decision trees and scripted responses. Good for simple, predictable interactions. Limited by what's explicitly programmed.

Retrieval-based chatbots Select from a database of pre-written responses based on user input. More flexible than rule-based but still limited to predefined answers.

Generative AI chatbots Use language models to generate unique responses. Can handle unexpected questions and maintain natural conversations.

Hybrid chatbots Combine multiple approaches. Might use rules for common queries, AI for complex ones, and human handoff for sensitive issues.

Task-oriented chatbots Focused on completing specific tasks: booking appointments, checking orders, processing returns. Structured conversations toward goals.

Conversational chatbots Designed for open-ended dialogue. Less focused on specific tasks, more on engaging conversation.

Customer support Handle common questions 24/7, reduce wait times, escalate complex issues to humans.

Lead generation Qualify website visitors, collect contact information, schedule sales calls.

E-commerce assistance Help shoppers find products, answer questions, guide purchases.

Appointment booking Schedule meetings, send reminders, handle rescheduling.

Internal support Answer employee questions about HR, IT, policies, and procedures.

Onboarding Guide new users through product features and setup.

Feedback collection Survey customers, gather reviews, identify issues.

Order tracking Provide shipping updates, handle delivery inquiries.

Define clear objectives What should the chatbot accomplish? Support tickets reduced? Leads captured? Sales assisted?

Know your users What questions do they ask? What language do they use? What are their expectations?

Design conversation flows Map out common paths. Where does the conversation start? Where should it end? What happens when things go off-script?

Provide escape routes Always offer ways to reach humans. Don't trap users in frustrating loops.

Set expectations Be clear about what the chatbot can and can't do. "I'm an AI assistant that can help with product questions..."

Test extensively Try to break it. Ask unexpected questions. Use wrong grammar. Test edge cases.

Monitor and improve Track where conversations fail. Update responses based on real user interactions.

The line between chatbots and AI agents is blurring, but distinctions remain:

Traditional chatbots:

• Respond to messages
• Primarily conversational
• Limited to predefined capabilities
• Reactive—wait for user input

AI agents:

• Take autonomous actions
• Use tools and APIs
• Execute multi-step tasks
• Proactive—can initiate actions

The evolution: Modern "chatbots" powered by LLMs often have agent capabilities. A customer service "chatbot" might:

• Check order status (tool use)
• Process refunds (action)
• Schedule callbacks (integration)
• Update records (automation)

When people say "chatbot" today, they might mean anything from a simple FAQ bot to a sophisticated AI agent. Context matters.