Activechat Manual
  • What is Activechat?
  • New? Start here
    • The basics
    • Set up your first project
    • Install the chat widget
    • Upload the knowledge
    • Explore the CRM
    • AI-assisted live chat
      • How to set context for AI hints
    • Live chat mobile app
    • Build your first automation
  • Conversational AI
    • For Customer Service Teams
    • For Product Managers
    • For Innovation Teams
    • For Marketers
    • For e-commerce
    • For developers
  • Help Guides
    • Setting up your team
    • Managing conversations
      • Customer attributes
      • User tags and segments
      • Searching for specific users
      • Agent tags (live chat groups and queues)
      • Triggering live chat sessions from the bot
      • Notifications with the TRIGGER block
    • Managing knowledge
      • Uploading business data
      • Question answering and live chat hints
      • Fine-tuning the large language model
    • Natural language automation
    • Building automations visually
      • Customizing your welcome message
      • Adding new skills
      • Navigating skills
      • Copying skills and blocks
      • Handling errors
    • Improving your virtual agent
    • Using live chat AI hints
    • Customizing automatic website page messages
    • Tracking website actions
    • Facebook Ads automation
      • How to set up a Facebook ads bot
      • How to use buttons and quick replies in a Facebook ads chatbot
    • Lead generation
    • Zapier integrations
    • Customizing your project
      • How to customize the chat widget
      • How to customize the Facebook chat widget
      • How to change bot settings
    • Pricing guide
  • Fundamentals
    • Terminology
      • Intents and entities
      • Contexts
      • Skills and events
        • Built-in system skills
          • /start
          • /default
          • /_default_fallback
          • /_start_live_chat
          • /_page_visit
          • /_error
      • Conversation elements
        • Messages
        • Buttons
        • Quick replies
        • Galleries / carousels
    • Messaging channels
      • Website chat widget
        • Installation
        • Customization
        • Voice input
      • Chat widget landing page
      • Facebook Messenger
        • Connect your page
        • 24 hour rule
        • Message tags
        • Persistent menu
      • Telegram
      • Email
      • Twilio SMS automation
    • Intents and bot skills
    • Conversation insights
    • Grow tools
      • Landing pages
      • Messenger links and QR codes
    • Broadcasting
  • Visual builder reference
    • Sending messages
      • TEXT
      • LISTEN
      • IMAGE
      • MEDIA
      • GALLERY
      • FILE
      • EMAIL
      • SMS
      • LEAD
    • Triggering events
      • SEND
      • CATCH
      • TRIGGER
      • LIVE CHAT
    • Manipulating data
      • DATA
      • ADD TAG
      • REMOVE TAG
      • JSON
      • STATUS
      • VALIDATION
    • Conditional logic
      • SWITCH
    • Timers and delays
      • TIMER
      • WAITFOR
      • WAITUNTIL
    • E-commerce blocks
      • CATEGORY
      • PRODUCT
      • VARIATIONS
      • SIMILAR
      • UPSELLS
      • CROSSSELS
      • Shopping carts
        • ADD TO CART
        • UPDATE CART
        • SHOW CART
        • CLEAR CART
        • CREATE ORDER
    • Natural Language
      • NLP
    • System attributes
    • System events
  • Integrations
    • Google services
      • Connect your Google account
      • Google Sheets
        • Searching and updating Google Sheets data
        • Building galleries with Google Sheets data
      • Google Calendar
        • Searching for events
        • Creating and updating events
    • Shopify
    • WooCommerce
    • Dialogflow
      • Building an agent
      • Using entities
      • Slot filling
      • Context management
      • E-commerce NLP
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On this page
  • Three essential chatbot technologies
  • Types of knowledge to use
  • How much data do you need?

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  1. Help Guides

Managing knowledge

PreviousNotifications with the TRIGGER blockNextUploading business data

Last updated 2 years ago

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One of the biggest challenges for every conversational designer is managing the underlying knowledge for their chatbot project. Working for a number of years with customers from every business vertical, we've seen a whole lot of approaches to this, from text files and whiteboard drawings to Google Docs, Notion and other advanced tools. But still, managing the knowledge for a complex conversational AI project that goes beyond a simple lead generation or F.A.Q. is a complex task that requires a lot of efforts.

That's why we decided to build an advanced knowledge management system inside our bot development platform. With the help of this system, developing a chatbot with complex conversations and managing the knowledge becomes a much easier task.

What's more, the knowledge management system is fully integrated with the chatbot development process, so you can test your chatbot directly from the knowledge management interface and make sure that the knowledge is correctly understood by the chatbot.

In this article, we will show you how to use the knowledge management system to develop a chatbot with complex conversations.

Three essential chatbot technologies

There are three basic techniques involved in building chatbots (and other conversational interfaces too):

  • decision trees ()

  • natural language understanding ()

  • natural language generation ()

Activechat combines all these technologies in a single interface, so that you can utilize them as needed, depending on exact requirements of your project. In this article we'll concentrate on the natural language generation aspect of conversational AI, explaining exact steps that you should take to get a smart natural language assistant with minimum effort.

Types of knowledge to use

Each conversational AI project is unique, as it has to follow the needs of a specific business and its customers. In early days of chatbot development, conversation designers had to program each interaction scenario individually, creating a framework of "skills" to respond to every customers' question. In our days, large language models (LLMs) like GPT-3 or BERT make it easy to skip this tedious process and extract information automatically, generating natural language responses to almost any relevant question that customers may ask about specific business or product. All you need to do is feed this information into your project, and we'll build and train an AI model to respond to questions based on this knowledge.

Once you do this, you will be able to use these automatically generated answers both as chatbot responses and as conversational .

Currently Activechat supports three types of business knowledge sources:

  • links to websites (F.A.Q.s, blogs, marketing websites, online shops, etc)

  • documentation in PDF or DOC files (SOPs, letters, business documents, etc)

  • existing conversation logs (from our own live chat engine or other customer service tools like Zendesk, Intercom, Drift, or Hubspot)

How much data do you need?

A simple answer: the more, the better. Since Activechat is using only specific data that you provide, the quality of generated responses will be affected by the volume of textual information that you upload to train the model.

For example, our own question answering bot is using the whole content of this manual, as well as our website, blog, and hundreds of previous conversation logs.

As a rule of thumb, a F.A.Q. with 10 to 100 questions will be a good start. If you are an online shop, you should definitely add your storefront URL. If your business has a blog, it can also be used as a great source of information and tone of voice.

visual scenarios
intent detection
question answering
hints for your live chat agents