Login
November 15, 2022|
Voiced by Amazon Polly |
Introduction to Conversational AI
A Conversational AI or Chatbot is a computer software that engages in natural language communication via voice or textual techniques, discerns the user’s purpose, and responds by organizational business rules and data.
Purpose of ChatBot
- The advantage of chatbots is that they can operate around the clock, every day of the week. Additionally, a chatbot may escalate difficult requests to your human support team if it ever has trouble understanding some users’ inputs and becomes stuck when attempting to respond to their questions or resolve issues.
- You may make as many requests at once as you would like to use chatbots. Chatbots considerably free up employees’ time by automating responses to most questions, enabling them to concentrate on jobs with a greater value-added.
- The data that chatbots generate about their performance is quite useful. A high engagement rate indicates that a chatbot is effective at fulfilling its goal of providing customer service.
Helping organizations transform their IT infrastructure with top-notch Cloud Computing services
- Cloud Migration
- Devops
- AIML & IoT
What does AWS Lex offer?
- Lex offers advanced deep learning functionalities, a fully managed service for creating conversational interfaces into any application using voice and text.
- Lex uses Automatic speech recognition (ASR) for converting speech to text and natural language understanding (NLU) to determine the text’s intended meaning. With these functionalities, you can quickly and easily create chatbots with incredibly engaging user interfaces and natural-sounding conversational interactions using Amazon Lex.
- Since Amazon Lex is a fully managed service, you do not have to worry about maintaining infrastructure because it scales automatically.
Lex Core Concepts and Terminologies
- Bots: Amazon Lex bots can converse in natural language and comprehend user input that is delivered in speech or through text. To carry out user data validation and fulfillment duties, you can develop Lambda functions and include them as code hooks in your intent settings.
- Intents: A user’s desired action is represented by an intent. To support one or more connected intents, you build a bot. You could, for instance, develop a bot that places orders for pizza and beverages. You must give the following necessary details for each intent.
a. Intent name– A uniquely identifying name for the intent. For example, Customerdetails (to get customer info or basic details). The Intent names must be unique within your account.
b. Sample utterances– How a user might speak or commands the intent. For example, a user might say “I need insurance” or “I would like to “.
c. Slot: Intent requires slots as parameters in the intent configuration, you can add slots. Amazon Lex asks the user for slot values during execution. Before Amazon Lex can carry out its purpose, the user must enter values into all the necessary slots. There are some prebuilt slots type to capture information such as email, phone number, address, and many more. Each slot has a type that can range from numbers and decimals to alphanumeric prebuilt slot types that help in capturing the required format of data without any hassle.
Architecture Diagram to Configure Lex Bot
- The following figure demonstrates a working model for Lex chatbot, the typical use case mentioned here is to develop a bot for customer service.
- The user journey starts with greetings from the bot which must be attached to the front end.
- Intents and slots are developed within the Lex console which helps to get the information from the client/customer.
- AWS Polly is inbuilt with lex to output speech format from text generated in lex.
- Lambda helps hook up code that requires validation or any custom input check.
- External API can be configured to get data from diverse sources and render back the information to the customer.
- A database is attached to the bot to get the user journey data and perform actions that could enhance the performance of the bot.
Conclusion
- With the increasing market demand for product-based industries there is a need for better service. AWS lex contributes to this sector by making customer service easier and on the go with the help of 24×7 available bots.
- These bots help user to skip the queue and perform needful tasks in just a matter of time.
- With the power of machine learning the bots can understand the user input and propose actions saving time and effort and reducing human intervention and turnaround time.
Get your new hires billable within 1-60 days. Experience our Capability Development Framework today.
- Cloud Training
- Customized Training
- Experiential Learning
About CloudThat
CloudThat is also the official AWS (Amazon Web Services) Advanced Consulting Partner and Training partner and Microsoft gold partner, helping people develop knowledge of the cloud and help their businesses aim for higher goals using best-in-industry cloud computing practices and expertise. We are on a mission to build a robust cloud computing ecosystem by disseminating knowledge on technological intricacies within the cloud space. Our blogs, webinars, case studies, and white papers enable all the stakeholders in the cloud computing sphere.
Drop a query if you have any questions regarding AWS Lex and I will get back to you quickly.
To get started, go through our Consultancy page and Managed Services Package that is CloudThat’s offerings.
FAQs
1. What are the most common use cases for Amazon Lex?
ANS: – The most common use cases include device control bots, informational bots, and self-service voice assistants.
2. In which AWS regions are Amazon Lex available?
ANS: – For a list of the supported Amazon Lex AWS regions, please visit Global Infrastructure Regions & AZs (amazon.com)
3. When do I use Amazon Polly vs. Amazon Lex?
ANS: – Amazon Polly converts text inputs to speech. Amazon Lex is a service for building conversational interfaces using voice and text.
WRITTEN BY Bineet Singh Kushwah
Bineet Singh Kushwah works as a Sr. Research Associate at CloudThat. His work revolves around data engineering, analytics, and machine learning projects. He is passionate about providing analytical solutions for business problems and deriving insights to enhance productivity. In a quest to learn and work with recent technologies, he spends the most time on upcoming data science trends and services in cloud platforms and keeps up with the advancements.
PREV
Comments