Two different approaches to conversation
A traditional chatbot follows defined rules, menus or intents. A language-model chatbot can interpret broader wording and generate a response, but it can also produce an unsupported answer. The choice is not a contest between old and new technology. It is a decision about how much variability the task needs and how much uncertainty the business can safely tolerate.
Where rules are a strength
Use deterministic flows when the permitted answers and transitions are known: choosing a department, collecting a reference or showing an approved policy link. These paths can be straightforward to test and explain. Their weakness is coverage: users may phrase requests in unexpected ways. Clear fallback and human contact options matter more than pretending that every message is understood.
Where language models can help
An AI assistant can summarize a request or locate relevant material in an approved knowledge base. Retrieval should return sources the user is allowed to see, and the interface should distinguish source evidence from generated explanation. When information is missing or conflicting, an unresolved state is safer than a fluent guess. Generation does not remove the need to maintain accurate source content.
A hybrid design is often practical
Keep identity checks, permissions and irreversible actions deterministic. Use the model for interpretation or drafting within that boundary. For example, classify a support message, present the proposed category and route the confirmed request through a normal backend. Do not let a chatbot grant itself tool access based on text in an uploaded document.
Compare using a real test set
Collect permitted examples that include spelling mistakes, ambiguous intent and requests outside scope. Compare correct completion, escalation quality, maintenance work and total operating cost. Review accessibility and data retention too. Select the simplest approach that handles the business task reliably, then keep an owner responsible for updating knowledge and reviewing failures.
A practical planning checklist
Ask which questions have one approved answer and which require interpretation. Identify the source owner for each answer and the permitted fallback when that source is missing. Compare a rule-based prototype and a source-grounded assistant using the same examples. Record the operational burden of both, including content maintenance, escalation and review, before selecting an architecture.
