Building a chatbot is the easy part to demonstrate. Knowing whether it works, for whom and at what risk, takes steady testing. This article summarises the practices that thoughtful guides on conversational AI tend to recommend.
Test before launch
Scripted tests. Write a list of typical questions and expected outcomes, and run it after every change. For rule-based and intent-based systems, this catches regressions quickly.
Messy input. Try typos, slang, very long messages, empty messages and several questions at once.
Edge and sensitive cases. Test requests the bot should decline, topics outside its scope and moments of distress. Decide in advance how the bot responds and when it hands over to a person.
Real users. A small pilot with volunteers reveals confusion that internal testers miss.
What to measure
- Task completion: did people achieve what they came for?
- Containment and escalation: how often did a conversation end without a human, and was that a success or an abandoned attempt?
- Fallback rate: how often did the bot fail to understand?
- Satisfaction: a simple rating or follow-up question, interpreted with care, since only a minority respond.
- Accuracy: a sample of conversations reviewed by people against trusted sources.
No single number tells the whole story. A high containment rate can hide frustrated users who gave up.
Reviewing language-model output
With generated replies, reviewers should look for factual errors, invented sources, tone problems and unsafe advice. Keep a standing set of difficult prompts and compare results over time. Review samples regularly, because behaviour can change when underlying models are updated.
Privacy and data handling
Collect only what you need. Tell users what is stored, for how long and who can see it. Avoid asking for sensitive information inside a chat unless there is a secure, justified reason. Check applicable laws in your region, and consult qualified advisers for legal questions, since this article is general information only.
Transparency and accessibility
Tell people they are talking to software. Offer a route to a human. Make sure the interface works with a keyboard and screen reader, and that colours and text sizes are readable.
A simple review routine
A small team can adopt a routine without special tools. Each week, pull a random sample of conversations, perhaps twenty, and have two people mark each as resolved, partly resolved or failed. Note why the failures happened: a missing answer, a misunderstanding, a confusing reply or a technical fault. Group the reasons and fix the largest group first. Over a few months this habit turns scattered complaints into a prioritised list and shows whether changes are working.
When not to automate
Some conversations should not be left to a bot, such as complaints involving harm, urgent safety matters or decisions with legal weight. Name these categories early and route them to a person straight away.
Keep improving
Treat launch as the start. Read transcripts, update the content, retire outdated answers and share findings with the whole team. A well-run chatbot is maintained like a publication, not shipped once and forgotten.
For background, read about rule-based and AI chatbots, revisit design basics or return to the home page.