When starting on a new bot, it’s often helpful to simply record how people normally engage in conversation for a single type of request, analyze those requests, and then extrapolate to a larger scope of requests. While this might seem insignificant at first, it’s essential to support concurrent and continuous development. As you make changes, you will notice that Amazon Lex automatically tracks a version for these resources so you know exactly what’s used for the particular version of the bot you’re testing. To promote reusability, intents and slot types are associated with an AWS account and can be used by multiple Amazon Lex bots. To learn how to do this, see the Amazon Lex Getting Started guide.Īs Jeff explained in his blog, Amazon Lex uses intents, slot types, and slots. We call it “CoffeeBot” and use an IAM role that has the appropriate permissions to invoke Amazon Lex. Using the Amazon Lex console, we create a custom bot. Let’s say that you need a voice bot to support conversations involving uncommon words such as when you order your latté: “May I have a triple mocha please?” Or, if you’re comfortable ordering a bot around, “Get me a triple mocha.” Would you like me to place this order and charge $55 on the card saved with this account? If no other means are available, you might simply use repetition. Additionally, with voice, you might be able to control the speech rate, for example, speak slower for emphasis. For example, you can use a confirmation when making significant changes to the order. In a voice interaction, you replace this convention with a new norm that assures the user that she is in control of when the order will be placed. You also can use text placement, whitespace, and capital letters for emphasis. In a chat experience, the convention is to list options in parentheses to let the user know what’s expected. With a web experience, the user experience can emphasize “Place your order” to clearly call it out as the recommended choice and to remind the user that this action is a commitment on the customer’s part. Would you like me to place this order? (yes/no) Consider these examples of how an order could be placed in each of the three modes: Depending on the goal and mode, the tools used for emphasis can differ. The rules for achieving clarity vary depending on the mode (web UI, text chat, or voice). It lays out the norms for presenting information and recommending choices. Some text interfaces might even support response cards, with images and buttons.įor the interface designer, emphasis is an essential tool. For a text interface, this may not be necessary. A helpful voice interface should anticipate that there are times when the user isn’t paying attention or simply can’t hear what was said, and offer the ability to repeat the last prompt or handle responses like “What?” or “Where were we?” gracefully. You also need to think about modality and medium. Simon Sinek urges us to start with why: Why does this chatbot exist? Good design starts with a clear goal: Who is the user, and what is that user trying to accomplish? Here’s what we’ve learned from the millions of interactions with Amazon Alexa. We recommend that you treat this section as an exploration of design considerations, not as a guide for bot design. Only interactions with real users can teach us what’s frustrating and what’s delightful. The basicsĬhatbot design is a nascent discipline with few established norms. Let’s see what else you need to do to build a chatbot. And although it’s a start, it’s hardly sufficient. What does it take to develop a functional chatbot using Amazon Lex? If you use one of the examples in the documentation, you can start interacting with your chatbot in about a minute or two. Amazon Lex’s advanced deep learning technology provides automatic speech recognition (ASR), for converting speech to text, and natural language understanding (NLU), to recognize the intent of text, so you can build applications with a highly engaging user experience. With Amazon Lex, the same deep learning technologies that power Amazon Alexa are now available to any developer, so you can quickly and easily build sophisticated, natural language conversational bots ( chatbots). As Jeff Barr showed in his introductory blog post, Amazon Lex is a service that allows developers to build conversational interfaces for voice and text into applications.
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