GETTING STARTED
Kore.ai XO Platform
Virtual Assistants Overview
Natural Language Processing (NLP)
Concepts and Terminology
Quick Start Guide
Accessing the Platform
Navigating the Kore.ai XO Platform
Building a Virtual Assistant
Help & Learning Resources
Release Notes
Current Version
Recent Updates
Previous Versions
Deprecations
Request a Feature
CONCEPTS
Design
Storyboard
Overview
FAQs
Conversation Designer
Overview
Dialog Tasks
Mock Scenes
Dialog Tasks
Overview
Navigate Dialog Tasks
Build Dialog Tasks
Node Types
Overview
Intent Node
Dialog Node
Dynamic Intent Node
GenAI Node
GenAI Prompt
Entity Node
Form Node
Confirmation Node
Message Nodes
Logic Node
Bot Action Node
Service Node
Webhook Node
Script Node
Process Node
Agent Transfer
Node Connections
Node Connections Setup
Sub-Intent Scoping
Entity Types
Entity Rules
User Prompts or Messages
Voice Call Properties
Knowledge AI
Introduction
Knowledge Graph
Introduction
Terminology
Build a Knowledge Graph
Manage FAQs
Knowledge Extraction
Import or Export Knowledge Graph
Prepare Data for Import
Importing Knowledge Graph
Exporting Knowledge Graph
Auto-Generate Knowledge Graph
Knowledge Graph Analysis
Answer from Documents
Alert Tasks
Small Talk
Digital Skills
Overview
Digital Forms
Digital Views
Introduction
Widgets
Panels
Session and Context Variables
Context Object
Intent Discovery
Train
NLP Optimization
ML Engine
Overview
Model Validation
FM Engine
KG Engine
Traits Engine
Ranking and Resolver
Training Validations
NLP Configurations
NLP Guidelines
LLM and Generative AI
Introduction
LLM Integration
Kore.ai XO GPT Module
Prompts & Requests Library
Co-Pilot Features
Dynamic Conversations Features
Intelligence
Introduction
Event Handlers
Contextual Memory
Contextual Intents
Interruption Management
Multi-intent Detection
Amending Entities
Default Conversations
Conversation Driven Dialog Builder
Sentinment Management
Tone Analysis
Default Standard Responses
Ignore Words & Field Memory
Test & Debug
Overview
Talk to Bot
Utterance Testing
Batch Testing
Conversation Testing
Conversation Testing Overview
Create a Test Suite
Test Editor
Test Case Assertion
Test Case Execution Summary
Glossary
Health and Monitoring
NLP Health
Flow Health
Integrations
Actions
Actions Overview
Asana
Configure
Templates
Azure OpenAI
Configure
Templates
BambooHR
Configure
Templates
Bitly
Configure
Templates
Confluence
Configure
Templates
DHL
Configure
Templates
Freshdesk
Configure
Templates
Freshservice
Configure
Templates
Google Maps
Configure
Templates
Here
Configure
Templates
HubSpot
Configure
Templates
JIRA
Configure
Templates
Microsoft Graph
Configure
Templates
Open AI
Configure
Templates
Salesforce
Configure
Templates
ServiceNow
Configure
Templates
Stripe
Configure
Templates
Shopify
Configure
Templates
Twilio
Configure
Templates
Zendesk
Configure
Templates
Agents
Agent Transfer Overview
Custom (BotKit)
Drift
Genesys
Intercom
NiceInContact
NiceInContact(User Hub)
Salesforce
ServiceNow
Configure Tokyo and Lower versions
Configure Utah and Higher versions
Unblu
External NLU Adapters
Overview
Dialogflow Engine
Test and Debug
Deploy
Channels
Publishing
Versioning
Analyze
Introduction
Dashboard Filters
Overview Dashboard
Conversations Dashboard
Users Dashboard
Performance Dashboard
Custom Dashboards
Introduction
Custom Meta Tags
Create Custom Dashboard
Create Custom Dashboard Filters
LLM and Generative AI Logs
NLP Insights
Task Execution Logs
Conversations History
Conversation Flows
Conversation Insights
Feedback Analytics
Usage Metrics
Containment Metrics
Universal Bots
Introduction
Universal Bot Definition
Universal Bot Creation
Training a Universal Bot
Universal Bot Customizations
Enabling Languages
Store
Manage Assistant
Team Collaboration
Plan & Usage
Overview
Usage Plans
Templates
Support Plans
Invoices
Authorization
Conversation Sessions
Multilingual Virtual Assistants
Get Started
Supported Components & Features
Manage Languages
Manage Translation Services
Multiingual Virtual Assistant Behavior
Feedback Survey
Masking PII Details
Variables
Collections
IVR Settings
General Settings
Assistant Management
Manage Namespace
Data
Overview
Data Table
Table Views
App Definitions
Data as Service
HOW TOs
Build a Travel Planning Assistant
Travel Assistant Overview
Create a Travel Virtual Assistant
Design Conversation Skills
Create an ‘Update Booking’ Task
Create a Change Flight Task
Build a Knowledge Graph
Schedule a Smart Alert
Design Digital Skills
Configure Digital Forms
Configure Digital Views
Train the Assistant
Use Traits
Use Patterns
Manage Context Switching
Deploy the Assistant
Use Bot Functions
Use Content Variables
Use Global Variables
Use Web SDK
Build a Banking Assistant
Design Conversation Skills
Create a Sample Banking Assistant
Create a Transfer Funds Task
Create a Update Balance Task
Create a Knowledge Graph
Set Up a Smart Alert
Design Digital Skills
Configure Digital Forms
Configure Digital Views
Add Data to Data Tables
Update Data in Data Tables
Add Data from Digital Forms
Train the Assistant
Composite Entities
Use Traits
Use Patterns for Intents & Entities
Manage Context Switching
Deploy the Assistant
Configure an Agent Transfer
Use Assistant Functions
Use Content Variables
Use Global Variables
Intent Scoping using Group Node
Analyze the Assistant
Create a Custom Dashboard
Use Custom Meta Tags in Filters
Migrate External Bots
Google Dialogflow Bot
APIs & SDKs
API Reference
API Introduction
Rate Limits
API List
koreUtil Libraries
SDK Reference
SDK Introduction
Web SDK
How the Web SDK Works
SDK Security
SDK Registration
Web Socket Connect and RTM
Tutorials
Widget SDK Tutorial
Web SDK Tutorial
BotKit SDK
BotKit SDK Deployment Guide
Installing the BotKit SDK
Using the BotKit SDK
SDK Events
SDK Functions
Tutorials
BotKit - Blue Prism
BotKit - Flight Search Sample VA
BotKit - Agent Transfer
  1. Docs
  2. Virtual Assistants
  3. Natural Language
  4. Kore.ai XO GPT Module

Kore.ai XO GPT Module

The new Kore.ai XO GPT Models module provides fine-tuned large language models optimized for enterprise conversational AI applications. These models have been evaluated and fine-tuned to be accurate, safe, and efficient for production deployment. Initial capabilities include Conversation Summarization and User Query Rephrasing. Additional models for features like Intent Resolution, Bot Response Rephrasing, Entity Co-referencing, etc., are planned in future updates.

Note: The Kore.ai XO GPT Module is currently available only for English language interactions. The respective models are available only in our global deployment (US Cloud – https://bots.kore.ai).

Advantages of Using Kore.ai XO GPT

The following are a few advantages of using Kore.ai XO GPT.

  • Accuracy: The XO GPT module leverages smaller foundation models, typically under 10 billion parameters, that have been fine-tuned specifically for conversational AI applications. By tuning smaller models rather than directly prompting larger generative models, the XO GPT Models achieve better accuracy, relevance, and interpretability for production deployment.
  • High Performance: The XO GPT Models are hosted along with the XO Platform and are relatively smaller in size. This results in faster response times, making them suitable for production use cases for digital and voice interactions.
  • Accelerate Time-to-Value with Pre-Tuned Models: The Kore.ai XO GPT Models come pre-fine-tuned for conversational AI use cases, eliminating the complex process of prompt engineering required for adopting commercial LLMs. Enterprises can rapidly deploy these models to start realizing value immediately without needing in-house machine learning expertise or long tuning cycles.
  • Data Security and Privacy: The Kore.ai XO GPT Models are fully integrated into the XO Platform, enabling the same enterprise-grade data confidentiality, privacy, and governance enforced across the XO stack.

Fine Tuning Process

Fine-tuning is an iterative process of taking a pre-trained LLM and adapting it to perform conversational AI tasks.

Kore.ai’s Model Fine-Tuning Process

  1. Data Collection: We gather a dataset specific to the desired task. This dataset serves as the training material for fine-tuning the LLM.
  2. Selecting a base LLM: The pre-trained LLM is loaded considering their purpose and the features to which they will be applied.
  3. Training Process: The training process utilizes the task-specific dataset to train the model, involving the model’s parameters adjustment to grasp the nuances of the target task.
  4. Validation and Iteration: The fine-tuned model is evaluated on a separate validation dataset to ensure it performs well. If necessary, we iterate through the fine-tuning process to achieve optimal results.

Features Supported by Kore.ai XO GPT

The Kore.ai XO GPT module supports the following features:

  • Conversation Summary: This model generates concise, natural language summaries of interactions between the virtual assistant, users, and human agents. It distills the key intents, entities, decisions, and outcomes into an easy-to-read synopsis. Companies can leverage conversation summarization to boost agent productivity, ensure process compliance, and create better contextual recommendations – without having to read lengthy transaction histories. It is pre-integrated with Kore.ai’s Contact Center platform. It is also extensible to third-party applications via API integration. Learn more.
  • Rephrase Dialog Responses: This feature sends all User Prompts, Error Prompts, and Bot Responses to the configured LLM with the conversation context, which depends on the configured number of user inputs. Responses are rephrased based on the context and user emotion, providing the end user with a more empathetic, natural, and contextual conversation experience. Learn more.
  • Rephrase User Query: This XO GPT model utilizes the bot domain knowledge and conversation history to expand and rephrase user queries for improved understanding by downstream NLP components. This includes better recognition of contextual intents, entity co-referencing, and more. Learn more.

 

Kore.ai XO GPT Module

The new Kore.ai XO GPT Models module provides fine-tuned large language models optimized for enterprise conversational AI applications. These models have been evaluated and fine-tuned to be accurate, safe, and efficient for production deployment. Initial capabilities include Conversation Summarization and User Query Rephrasing. Additional models for features like Intent Resolution, Bot Response Rephrasing, Entity Co-referencing, etc., are planned in future updates.

Note: The Kore.ai XO GPT Module is currently available only for English language interactions. The respective models are available only in our global deployment (US Cloud – https://bots.kore.ai).

Advantages of Using Kore.ai XO GPT

The following are a few advantages of using Kore.ai XO GPT.

  • Accuracy: The XO GPT module leverages smaller foundation models, typically under 10 billion parameters, that have been fine-tuned specifically for conversational AI applications. By tuning smaller models rather than directly prompting larger generative models, the XO GPT Models achieve better accuracy, relevance, and interpretability for production deployment.
  • High Performance: The XO GPT Models are hosted along with the XO Platform and are relatively smaller in size. This results in faster response times, making them suitable for production use cases for digital and voice interactions.
  • Accelerate Time-to-Value with Pre-Tuned Models: The Kore.ai XO GPT Models come pre-fine-tuned for conversational AI use cases, eliminating the complex process of prompt engineering required for adopting commercial LLMs. Enterprises can rapidly deploy these models to start realizing value immediately without needing in-house machine learning expertise or long tuning cycles.
  • Data Security and Privacy: The Kore.ai XO GPT Models are fully integrated into the XO Platform, enabling the same enterprise-grade data confidentiality, privacy, and governance enforced across the XO stack.

Fine Tuning Process

Fine-tuning is an iterative process of taking a pre-trained LLM and adapting it to perform conversational AI tasks.

Kore.ai’s Model Fine-Tuning Process

  1. Data Collection: We gather a dataset specific to the desired task. This dataset serves as the training material for fine-tuning the LLM.
  2. Selecting a base LLM: The pre-trained LLM is loaded considering their purpose and the features to which they will be applied.
  3. Training Process: The training process utilizes the task-specific dataset to train the model, involving the model’s parameters adjustment to grasp the nuances of the target task.
  4. Validation and Iteration: The fine-tuned model is evaluated on a separate validation dataset to ensure it performs well. If necessary, we iterate through the fine-tuning process to achieve optimal results.

Features Supported by Kore.ai XO GPT

The Kore.ai XO GPT module supports the following features:

  • Conversation Summary: This model generates concise, natural language summaries of interactions between the virtual assistant, users, and human agents. It distills the key intents, entities, decisions, and outcomes into an easy-to-read synopsis. Companies can leverage conversation summarization to boost agent productivity, ensure process compliance, and create better contextual recommendations – without having to read lengthy transaction histories. It is pre-integrated with Kore.ai’s Contact Center platform. It is also extensible to third-party applications via API integration. Learn more.
  • Rephrase Dialog Responses: This feature sends all User Prompts, Error Prompts, and Bot Responses to the configured LLM with the conversation context, which depends on the configured number of user inputs. Responses are rephrased based on the context and user emotion, providing the end user with a more empathetic, natural, and contextual conversation experience. Learn more.
  • Rephrase User Query: This XO GPT model utilizes the bot domain knowledge and conversation history to expand and rephrase user queries for improved understanding by downstream NLP components. This includes better recognition of contextual intents, entity co-referencing, and more. Learn more.

 

메뉴