Here are the steps for creating an intent in a skill.

1. Click Intents in the left navbar.
2. If you already have defined your intents in a CSV file, click Import Intents. Import Intents from a CSV File describes this file's format. Otherwise, click Add Intent. Your skill needs at least two intents.
3. Click to enter a descriptive name or phrase for the intent in the Conversation Name field. For example, if the intent name is callAgent, the conversation name would be Talk to a customer representative. When the skill can't resolve a message to an intent, it outputs the user-friendly names and phrases that you enter into the Conversation Name field as the options that are listed in the Do you want to disambiguation messages described in How Confidence Win Margin Works and Configure the Intent and Q&A Routing.
4. Add the intent name in the Name field. If you don't enter a conversation name, then the Name field value is used instead. Keep in mind that a short name with no end punctuation might not contribute to the user experience. The intent name displays in the Conversation Name field for skills built with prior versions of Digital Assistant.
   Note
   
   In naming your intents, do not use system. as a prefix. system. is a namespace that's reserved for the intents that we provide. Because intents with this prefix are handled differently by Trainer Tm, using it may cause your intents to resolve in unexpected ways.
5. Add a description of the intent. Your description should focus on what makes the intent unique and the task or actions it performs.
6. If this is an answer intent, add a short answer to the Answer field.
7. Optionally, in the Annotations field, add one or more tags for the intent to categorize it in a way that is useful for you. You can use any words of your choosing.

   Tip:

   On the Intents page, you can filter the display of intents by annotation.
8. Start building the training corpus by adding example utterances that illustrate the meaning behind the intent. To ensure optimal intent resolution, use terms, wording, and phrasing specific to the individual intent. Ideally, you should base your training data on real-world phrases. You can save your utterances by clicking Enter or by clicking outside of the input field. To manage the training set, select a row to access the Edit () and Delete () functions.
   If your skill supports multiple native languages, augment the training set with phrases in the secondary languages to strengthen the model's accuracy in this and all other native languages supported by the skill.
   

   
   

   
   
   See Build Your Training Corpus for tips on building an effective training corpus.
   To allow your skill to cleanly distinguish between intents, create an intent that resolves inappropriate user input or gibberish.
   While utterances can be added to an existing intent manually or by importing a CSV, they can also be assigned to intents through data manufacturing jobs and the Insights retrainer.
9. In the Auto-Complete Suggestions field, enter a set of suggested phrases that help the user enter an appropriately worded request. Do not add the entire set of training data. Add a set of phrases that represent ideal user requests instead. Adding too broad a set of utterances may not only confuse users, but may also result in unexpected behavior.
   This is an optional step. This function is only supported by the Oracle Web Channel.
10. Add an entity if the intent needs one to resolve the user input. To find out how, see Add Entities to Intents.
11. To teach your skill how to comprehend user input using the set of utterances that you’ve provided so far, click Train, choose a model and then click Submit.
    As described in Which Training Model Should I Use?, we provide two models that learn from your corpus: Trainer Ht and Trainer Tm. Each uses a different algorithm to reconcile the user input against your intents. Trainer Ht uses pattern matching while Trainer Tm a machine learning algorithm which uses word vectors. Both skills that use Digital Assistant's native language support and skills with answer intents require Trainer TM.
    You’d typically follow this process:

    1. Create the initial training corpus.

    2. Train with Trainer Ht. You should start with Trainer Ht because it doesn’t require a large set of utterances. As long as there are enough utterances to disambiguate the intents, your skill will be able to resolve user input.

       If you get a Something’s gone wrong message when you try to train your skill, then you may not have added a sufficient number of utterances to support training. First off, make sure that you have at least two intents with at least two (or preferable more) utterances each. If you haven’t added enough utterances, add a few more then train your skill.

    3. Refine your corpus, retrain with Trainer Ht. Repeat as necessary—training is an iterative process.

    4. Train with Trainer Tm. Use this trainer when you’ve accumulated a robust set of intents.

    The Training Needed displays whenever you add an intent or when you update an intent by adding, changing, or deleting its utterances. To bring the training up to date, choose a training model and then click Train. The model displays an exclamation point whenever it needs training. When its training is current, it displays a check mark.

12. Click Test Utterances (located at the upper left) to open the Utterance Tester. Select the target language, then enter utterances similar to those in your training set. The Utterance Tester returns the confidence level for this utterance and enables you to assign the utterance to an intent, or add it as a test case.
    To log your intent testing results, enable the conversation intent logging (Settings > General > Enable Insights) .
13. Click Validate and review the validation messages for errors such as too few utterances and for guidance on applying best practices like adding an unresolvedIntent intent.