How do rule-based and LLM chatbots differ?
A rule-based bot walks a decision tree: it matches keywords, offers buttons, and fails politely on anything unscripted. It is cheap, predictable and frustrating off the happy path. An LLM chatbot generates its answers from a language model grounded in your content, so it handles phrasing it has never seen. The trade is control for coverage: scripts never surprise you, models handle the long tail of questions no script anticipated. The craft of grounding, tone and escalation is conversational AI.
What is the difference between a chatbot and an AI agent?
Action. A chatbot’s output is text; an AI agent can also do things: check a real order status, book an appointment, update a record, escalate with context. Most deployments worth paying for in 2026 are agents wearing a chat interface. When evaluating a vendor, ask what the system can do, not what it can say: an AI receptionist that cannot book is a brochure that talks.
Where do chatbots pay off for a mid-sized company?
Where the same questions arrive repeatedly and the answers live in known systems: order status, opening hours, policy lookups, first-line support triage and lead qualification. A deployment pays off when it resolves a meaningful share of conversations end to end and hands the rest to a person with the transcript attached. The number to manage is resolution rate with escalation quality, not conversation volume. On the phone, the same logic runs through voice agents and contact center AI.
What goes wrong with chatbot projects?
The classic failures are stale grounding, no escalation path, and silent wrongness: a bot confidently answering from last year’s policy. A chatbot that cannot say “I do not know, let me hand you over” will eventually say something expensive. Keep its knowledge sourced from systems that stay current, measure hallucination on a sample, and review the conversations it loses: they are a free list of what customers could not find.