Pre-Launch Checklist: How to Instrument and Test a Support Chatbot Right

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Shipping a support chatbot without proper preparation can lead to hard-to-debug failures, since real user behaviour is far messier than controlled test environments. Developers should ensure distributed tracing across all services — including authentication, API gateways, retrieval, and model providers — using shared request and trace IDs. Conversation logs must be handled carefully to avoid storing sensitive customer data such as payment details or personal information unnecessarily. Test sets should be built from real support queries and cover edge cases like misspellings, multi-turn exchanges, out-of-scope requests, and prompt injection attempts. Versioning the evaluation set is essential so that changes to prompts or models can be reliably assessed rather than guessed at.

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