Chatbot
AI-powered dialogue systems that answer user requests automatically – from rule-based FAQ bots to LLM-based assistants with natural language understanding.
Chatbots have evolved from simple FAQ scripts to intelligent conversation partners. Thanks to large language models (LLMs) like GPT, modern chatbots can hold natural conversations, understand complex questions and give contextual answers. For businesses they offer 24/7 support, lead qualification and internal knowledge assistants – while reducing cost.
What is Chatbot?
A chatbot is software that communicates with users automatically in natural language – by text or voice. Two main types: rule-based chatbots follow predefined decision trees (if–then) and only handle programmed scenarios. AI chatbots use natural language processing (NLP) and large language models (LLMs) to detect intent, understand context and generate answers. Hybrid approaches combine both: AI for understanding, rules for critical processes (orders, bookings).
How does Chatbot work?
An AI chatbot processes requests in steps: 1) NLU (Natural Language Understanding): the message is analysed – intent and entities are extracted. 2) Dialog management: the next action is chosen from intent, context and history. 3) Response generation: the answer is produced – from templates, database or LLM. 4) Channel: the reply is sent (website, WhatsApp, Teams). RAG (Retrieval-Augmented Generation) chatbots also search a knowledge base to ground the LLM answer in current, company-specific information.
Practical Examples
Support bot: Answers ~70% of common questions (delivery, returns, FAQ) and hands complex cases to humans.
RAG knowledge assistant: Internal bot that answers from company docs, manuals and policies.
Lead qualifier: Asks website visitors targeted questions, qualifies by budget and timeline, and books meetings.
HR bot: Answers employee questions on leave, benefits, pay and policies.
E-commerce advisor: Recommends products, answers product questions and helps at checkout.
Typical Use Cases
Customer service: Automated answers to standard queries with escalation to humans
Sales: Lead generation, qualification and booking on the website
Internal knowledge: AI access to company documentation and processes
E-commerce: Product advice, sizing and order status
IT helpdesk: Automated handling of common issues (password reset, VPN, installs)
Advantages and Disadvantages
Advantages
- 24/7 availability: Instant answers at any time
- Cost-effective: One bot can handle hundreds of conversations at once
- Consistency: Same answer quality for every customer
- Data: Conversations provide insights into needs and pain points
- Relief for staff: Teams focus on complex cases
Disadvantages
- Hallucinations: LLM chatbots can give plausible but wrong answers
- Limited empathy: For emotional issues humans are often better
- Implementation: A good chatbot needs training, testing and ongoing tuning
- Frustration: Poor chatbots that don’t understand hurt the experience
Frequently Asked Questions about Chatbot
What does an AI chatbot cost?
How do I prevent hallucinations?
Which AI models suit chatbots?
Related Terms
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