AI chatbot

01why it matters for a business

Chatbots are often the first AI project a company considers because the use case is easy to picture: answer customer questions around the clock. The older generation followed scripted decision trees and frustrated anyone who asked something off script. Language models removed that limit but introduced a new one: a model will answer confidently even when it does not know, unless it is grounded in your content and told when to stop.

A chatbot is a channel, not a strategy. Its value depends on what it can draw on and what happens when it cannot help. The executive questions are: what content will it answer from, what is it forbidden to discuss, what does it hand to a person and how fast, and how will you know whether customers got a correct answer.

02what it looks like in practice

A multi-location dental group adds a chatbot to its website to answer questions about services, insurance accepted, hours, and preparation for procedures. It answers only from an approved knowledge base using retrieval, declines to give clinical advice, and offers to book a call for anything else. A weekly review of unanswered questions shows which content to add. Letting it book appointments requires a tool connected to the scheduling system, which is the point where it starts to become an agent.

03common mistakes

• Letting it answer from the model's general knowledge. Ground it in approved content or it will invent policies.
• No handoff to a person, or a handoff that loses the conversation so the customer has to start over.
• Launching without reviewing transcripts. Read real conversations weekly for the first months.
• Measuring deflection alone. A chatbot that deflects contacts by giving wrong answers is costing you customers.

04related terms

• AI agentAn AI agent is software that uses a language model to pursue a goal by choosing its own next steps: it reads the situation, picks a tool or action, checks the result, and repeats until the task is done or it needs a person.
• Retrieval-augmented generation (RAG)Retrieval-augmented generation (RAG) is a technique in which an AI system first searches your own documents or data for passages relevant to a question, then gives those passages to a language model to write its answer.
• AI hallucinationA hallucination is output from an AI model that is fluent and confident but false or unsupported: an invented fact, a made-up citation, a policy that does not exist, or a number that appears nowhere in the source.
• AI guardrailsGuardrails are the controls around an AI system that keep its inputs, outputs, and actions within acceptable limits: input filtering, output checks for policy, format, and sensitive data, limits on which tools and data it can reach, spending caps, and rules that route risky cases to a person.
• AI knowledge baseAn AI knowledge base is a company's documents, policies, procedures, and records organized so an AI assistant can search them and answer questions with citations.

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