This article was originally published on QBox’s blog, prior to Cyara’s acquisition of QBox. Learn more about Cyara + QBox.
Wondering how to fix your IBM Watson Assistant chatbot?
Testing and fixing a IBM Watson chatbot can be very time consuming. Where is it going wrong? Are my chatbot fixes working? Why are my fixes now causing other problems? Is there a way of continually monitoring my chatbot’s performance?
Cyara makes it possible for businesses to optimize chatbot performance through every stage of the development lifecycle.
We’ve put together a very handy tutorial for IBM Watson chatbot builders. This guide takes you right from inputting your training data right through to drilling down into why utterances and intents aren’t working – and, of course, how to put them right.
What you’ll learn
- Testing IBM Watson data
- Improving correctness and confidence—the key KPIs
- Intent scoring and upgrading
- Individual intent and utterance analysis
- Validating fixes and checking for regressions
- Tracking improvements to your model
If you’re looking for a guide rich in detail and with lots of practical guidance, it’s worth giving this one a watch.
We hope you found the video useful. If you have any questions about how you can improve your NLP chatbot, contact us. We’d be happy to show you what our solutions can do!
