As you have now identified some issues in your NLU performance it is time to augment the training data to improve the NLU performance.
This step only applies if you used the Test Case Wizard to download the full training data from your NLU engine in the previous step of this tutorial. Otherwise you will have to apply the changes to your training data in your NLU engine itself - here are links to the documentation of some of the supported providers:
- [Microsoft LUIS](https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-how-to-add-intents#working-with-an-individual-utterance) - [IBM Watson](https://console.bluemix.net/docs/services/assistant/intents.html#editing-intents) - [Google Dialogflow](https://dialogflow.com/docs/intents/training-phrases) - [Wit](https://wit.ai/docs/quickstart).ai - [Amazon Lex](https://docs.aws.amazon.com/lex/latest/dg/ex-utterances.html) - [RASA](https://rasa.com/docs/nlu/master/dataformat/) - [QnA Maker](https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/edit-knowledge-base#add-a-qna-pair)
With the help of Botium you now have decided what actions to take
in general, there are three possible actions to augment your training data:
Add additional user examples for specific intents
Remove user examples from intents
Move user examples from one intent to the other
You can use the Botium Test Case Designer to perform these actions.