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Jordan Bragg discusses using entry-points, breadth-first scanning, and operation tagging to demystify the domain, see where to dive deeper, and uncover what technical debt may exist.
Even when designing a Minimum Viable Architecture (MVA), developers must consider resource location, especially when mobile apps are part of a distributed system. Distributing the data and processing can introduce new challenges if location is not part of the decision making criteria.
In a web-based service, a slowdown in request processing can eventually make your service unavailable. Chances are, not all requests need to be processed right away. Some of them just need an acknowledgement of receipt. Have you ever asked yourself: “Would I benefit from asynchronous processing of requests? If so, how would I make such a change in a live, large-scale mission critical system?”
In this podcast, Shane Hastie, spoke to Missy Lawrence Johnston about the human skills engineers and engineering leaders need to build in the digital age.
At QCon Plus November 2021, Nora Jones, CEO and founder of Jeli, talked about how to build production readiness reviews (PRR) with emphasis on context and psychological safety. Her talk focused on the particulars of a PRR process that relates to incidents.
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InfoQ Homepage News Amazon Is Adding Visual Conversation Builder for Amazon Lex
Sep 19, 2022 1 min read
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Amazon is introducing the Visual Conversation Builder for Amazon Lex, a drag and drop interface to visualize and build conversation flows in a no-code environment. The Visual Conversation Builder greatly simplifies bot design.
Amazon Lex is a fully managed artificial intelligence service with advanced natural language models to design, build, test, and deploy conversational interfaces in applications. Amazon Lex provides high-quality speech recognition and language understanding capabilities. The visual builder, allows to build and manage complex conversations with dynamic paths. By adding conditions directly to Lex bot, and managing the conversation path dynamically based on user input and business knowledge, all within a no-code environment.
According to Amazon, no machine learning expertise is necessary to use Amazon Lex. Developers can declaratively specify the conversation flow and Amazon Lex will take care of the speech recognition and natural language understanding functionality. Developers provide some sample utterances in plain English and the different parameters that they would like to collect from their user with the corresponding prompts. The language model gets built automatically.
To create a bot, you will first define the actions performed by the bot. These actions are the intents that need to be fulfilled by the bot. For each intent, you will add sample utterances and slots. Utterances are phrases that invoke the intent. Slots are input data required to fulfill the intent. Lastly, you will provide the business logic necessary to execute the action. An Amazon Lex bot can be created both via Console and REST APIs.
Chatbots are majorly incorporated into messaging applications, websites, mobile applications and other digital devices for interacting as digital assistants through text or text-to-speech functionalities. They offer various benefits, such as improved efficiency of business operations, customer engagement, branding and advertisement, data privacy and compliance, payment processing and automatic lead generation and qualification. Other visual chatbot builder alternatives are IBM Watson Chatbot Builder, Microsoft Bot Framework, Meta Bots for Workplace, among others.
As buying journeys grow more complex, removing friction from the digital experience is essential. Chatbots enhance the buyer and customer experience by providing a channel for site visitors to interact with brands 24/7 without the need for human intervention.
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