Initial concepts
So, what we can do with the Platform?
How do I know if my company needs a chatbot?
Register and login
First Steps - Creating your project
Choose your plan
Profile
Permission System
Project Dashboard
Platform Glossary
Changing the Platform Language
2-Factor Authentication
Invalid authentication code
General settings
Artificial Intelligence
Agent Builder
Zero Shot Learning
WeniGPT
Weni Platform AI Module
Repository - Overview
What is an Intelligence?
Intents and Entities
Hands-on
Creating an Intelligence
Training your Intelligence
Strength of Intelligence
Testing your intelligence
Interactions
Translating your dataset
Versioning
Settings
API
Integrating Intelligence into a Project on the Weni Platform
Introduction to Content Intelligence
Integrating a Content Intelligence
Interface Updates
Glossary
Guidance and Best Practices
Flows
Expressions and Variables Introduction
Variables Glossary
Expressions Glossary
Flows Creation
Flows introduction
Flow editor and tools
Action cards
Decision cards
Adding Media to the message
Call Webhook: Making requests to external services
Split by Intent: Using Classification Artificial Intelligence
Import and export flows
Using expressions to capture the user's location
Viewing reports on the platform
Route markers
WhatsApp Message Card
Studio
Contacts and Messages
Groups
Messages
Triggers and Campaigns
Adding a trigger
Triggers Types
Tell a flow to ignore triggers and keywords
Campaign introduction
How to create a Campaign
Editing events
Creating contact from an external Webhook
Contact history
How to Download and Extract Archived Data
Channels
Settings
How to connect and talk to the bot through the settings
Adding a Facebook Channel
Adding a Viber channel
How to Create an SMS Channel - For Developers (RapidPro)
Web Chat Channel
General API concepts and Integrations
How to create a channel on twitter
How to create a channel on Instagram
How to create an SMS channel
Adding ticket creation fields in Zendesk
Adding Discord as a channel
Creating a Slack Channel
Adding a Viber channel (RapidPro)
Creating a Microsoft Teams channel
Weni Integrations
How to Use the Applications Module
How to Create a Web Channel
Adding a Telegram channel
How to create a channel with WhatsApp Demo
Whatsapp: Weni Express Integration
Whatsapp: How to create Template Messages
WhatsApp Template Messages: Impediments and Configurations
Supported Media Sending - WhatsApp Cloud
Whatsapp Business API
Integrations
Human Attendance
Weni Chats: Introduction to the Chats module
Weni Chats: Setting Up Human Attendance
Weni Chats: Human Service Dashboard
Weni Chats: Human Service Management
Weni Chats: Attendance distribution rule
Weni Chats: Using active triggering of flows
Weni Chats: CoPilot
Ticketer: Ticketer on Rapid Pro
Ticketer: How to integrate Rocket.Chat as a ticket service on the Weni Plataform
How to send message templates through RocketChat
RocketChat call routing
RCAdmin API: Agent-Activity
U-Partners - Proper use of features
Using groups to organize human attendance
Data and BI
How to Install and Use the Weni Data Connector for Power BI
Incremental Update - Power BI
Explore Weni's Database Documentation
Tips for Data Modeling in Power BI
Filter using Contact Fields in Power BI
UX Writing
- All Categories
- Artificial Intelligence
- Glossary
Glossary
Updated by Manu da Silva
In this article, you will find the meaning of some common terms and expressions used in Weni. Most of these terms are found in NLU platforms, but some are specific to the Weni community, making it very important for all users to understand them.
- Precision: A metric obtained from intelligence tests. It refers to the ratio of correctly predicted positive observations to the total predicted positive observations.
- Algorithm: Generally, it is a block of code that performs a specific action given an input. In the context of NLU, the algorithm specifies how the training data will be processed to generate a model that will make user predictions.
- Confidence: The certainty rate of your bot regarding its classification. It indicates how confident the intelligence is in interpreting the response, based on the trained dataset.
- Dataset: A dataset is a collection of all the example phrases registered in Weni. The dataset "teaches" the bot what it should understand about each intent.
- Entity: Represents a specific piece of information extracted from the user's input. Entities can be used to add additional levels of abstraction for a given context.
- Test: The process used to validate your intelligence's dataset. In both Quick Test and the Tests tab, users can analyze whether the training has been effective based on certain metrics.
- Interactions: A feature that gathers all phrases sent to the intelligence along with their prediction data. It is typically used to improve datasets based on these phrases, as it allows users to add these phrases to training and/or test datasets.
- Integration (API): The way external services communicate with BotHub to use predictions. We can integrate these other platforms with BotHub by providing the intelligence's authorization data.
- Intent: Represents the goal of a user's input. Usually, the user defines the intent for each type of response that the intelligence will need to predict.
- NLP (Natural Language Processing): Technology that handles interactions between humans and computers by processing an input in natural language and interpreting it so that the machine can take actions based on that input.
- Recall: A metric obtained from intelligence tests. It refers to the ratio of correctly predicted observations to all observations made in the test.
- Training/Test Phrases: A training or test phrase is a simulation of an end-user input sentence. They are usually related to the main scope of the intelligence and are used to feed its datasets.
- Training: The process of "teaching" new phrases from your dataset to improve intelligence. Running training means generating a new model (a new intelligence) for your bot based on all the registered training phrases.
- Model: Essentially a set of weights generated by training. The training adjusts the model based on the dataset so that it learns the patterns and can thus answer questions it has never seen before (in our case, classify phrases). The model is the intelligence of the bot, and it performs the phrase classification. Weni abstracts the use of the model to make it simple for the user.
- Versions: A feature that allows users to work with multiple versions of the same dataset, making changes without interference between them.