Build an AI chatbot by defining the task it should complete and the requests it should decline or escalate. Start with a bounded workflow and evaluate it before adding more autonomy.
1. Define the job and evidence
List the intended audience, permitted topics, authoritative sources, and expected outcomes. Include ambiguous questions and requests the bot cannot answer. A product support bot should use the relevant policy version rather than invent an answer when evidence is missing.
2. Evaluate the model on your tasks
Use reviewed examples and compare correctness, latency, cost, and handling of unsupported requests. Check official documentation for the exact model version and required features. No model family is universally best for every chatbot.
3. Enforce data and action boundaries
Apply authorization before retrieving account data or executing tools. Keep confidential information out of unnecessary logs. Treat user messages, documents, and tool results as untrusted inputs, and require appropriate confirmation for consequential actions.
4. Make escalation work
Test that a human handoff creates the right case and preserves enough context without excessive sensitive data. Distinguish a suggested action from a completed one in the response.
5. Monitor the complete journey
Trace model, retrieval, and tool operations with a request identifier. Review task completion and failed handoffs alongside latency and cost. Keep release and prompt versions so a regression can be reproduced.
References and further reading
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.