How Conversational AI Work? A Detail Guide on Conversational AI
- 12 Jan 2024
What is Conversational AI?
Conversational AI(Artificial Intelligence) is a process or set of technologies which helps to enable your machine to process, understand and respond naturally to text or voice inputs. It’s a part of AI which allows computer to understand & process human languages and eServeCloud Cloud Conversational AI module power new generative AI Capabilities. It’s refer to technologies like chatbots & virtual assistants.
The Conversational AI uses high volumes of data, machine learning (ML) module and NLP (natural language processing) module to understand and recognized the speech & text inputs, understand intent and understand various languages and respond it into various human conversational language.
What are the component of Conversational AI?
Conversational AI is a combination of NLP & ML. NLP work constantly with machine learning process with constant feedback loop to improve the AI algorithms in natural way. Conversational AI allow it to process, understand and generate response in natural way.
Machine Learning (ML) : It is a sub-field of AI (Artificial Intelligence) which is developed by set of algorithm, it works with the help of its own hidden patterns of the datasets which improve themselves with experience of it’s own datasets. It’s all depend upon the input of the data as it grow the pattern works better. This is the technology which used in many applications from image and speech recognition to natural language processing, recommendation systems, fraud detection, portfolio optimization, automated task, and many more.
Natural Language Processing : It is the method of understanding human language and analyze them so that it can automatically perform repetitive tasks. For example, machine translation, summarization, ticket classification, and spell check. Deep learning will also advance the NLP capabilities of Conversational AI even further. NLP process with 4 steps that can be broken down into 4 practice.
Input Generations : First, a user provides either a text or voice input through a website or an application. For spoken words, automatic speech recognition (ASR), also known as speech recognition, will convert it into the text to be read by the computer
Input Analysis : Next, the conversation engine will use natural language processing (NLP) to decipher the meaning and derive the text’s intent.
Dialogue /Output Management : During this step, the application formulates a response based on intent using Dialog Management. Further, natural language generation (NLG), a part of NLP, orchestrates and converts the response into a human-understandable format.
Reinforcement learning. : Finally, machine learning (ML) algorithms learn from experience and refine response overtime to provide a better response.
How conversational AI works?
Conversational AI works with the combination of Natural Language Processing Module and Machine Learning Module, including Foundation Models. As we all know, Conversational AI works with large amount of data such as text or speech. This data helps to understand and process human language and then this system interact with humans in natural way. The important point is such module constantly learn for them the data and its interactions and improve the response by that learning.
What are the benefit of Conversational AI?
Conversational AI as a technology can boost the team’s effectiveness in many ways. Which include customer engagement with 24/7 availability and ongoing customer support and assistance. Customer interaction with the company. Enhancement of the team bounding and many more. As a company you can use our Conversational AI enabled tools eServeCloud to streamline your customer communication by adding AI enabled helpdesk to your website which will help to automate your task by offering human like interactions and your CS (Customer Service Team) will engage with another important task.
How eServeCloud using NLP & ML Module to developed Conversational AI Module for your Business?
We chunk your data intelligently and create embedding vectors which are stored in a vector database. Further, user query is converted to embedding to retrieve the closest chunks against the vector database. Then we use the retrieved information as input to LLM models with instructions to get the desired answer which user is looking for.
Here in this diagram, you can how a business can easy creates or updates documents and Data ingestions with it and then we intelligently chunk the data and feed into our own LLM Model which help in AI Assistant and customer can view there query easily.
So eServeCoud use the above technology to initiate the users data.
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