What This Looks Like in Practice
AI chatbots handle conversational interactions — answering questions, guiding users through a process, or providing general assistance — built on an underlying language model rather than the scripted, rule-based bots of a decade ago.
How Teams Are Approaching This
Businesses deploy chatbots for customer-facing support, internal help desks, and product onboarding, typically layering a system prompt and sometimes a knowledge base (via RAG) on top of a general-purpose model to specialize its behavior for their specific context.
Considerations
The gap between a demo chatbot and a genuinely reliable production one is usually in edge-case handling and knowing when to escalate to a human — a chatbot confidently giving a wrong answer is often worse than one that clearly says it doesn't know.
Related Pages
Frequently Asked
Do I need to train a custom model to build a chatbot?
Usually not — most chatbots are built on an existing general-purpose model with a system prompt and, often, RAG for grounding in specific knowledge, rather than training a model from scratch.
What's the biggest risk with a customer-facing chatbot?
Confidently giving wrong information; see our Hallucination in AI page and ensure a clear escalation path to a human for anything the bot is uncertain about.
Where can I find chatbot-building platforms?
See our Platforms and Tools directories.
How is this different from customer support use case specifically?
This page covers chatbots as a technology pattern; see our Customer Support page for that specific business function's fuller picture.