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  • Getting Started
    • Introduction
    • Sign up to Developer Edition
    • Build Your First Agent
    • Developer Support
  • Core Concepts
    • Agent
      • Knowledge
      • Webhook
    • PII Masking
    • Sub-Agent
    • Intent
    • Workflow
      • Node
        • Input
        • Output
        • Loader
        • Display
        • API Node
        • Web Crawler
        • Table Write
        • Table Read
        • Ruleset
        • Upload Document
        • Javascript
        • Workflow
        • Loop
        • Document To Image
        • External Database
        • Storage Write
        • Storage Read
        • Fetch Document
        • Prompt
        • RAG Query
        • Vector Search
        • Emit Event
    • RAG
    • Model Hub
      • Entity Recognizers
    • Data Gateway
    • Rulesets
    • Code Snippets
    • Tables
    • Storage
    • Widget
  • Overview of GenAI
    • Introduction
    • Key concepts
      • Intent Classification
      • Inference
      • Generative AI Models
      • Large Language Models (LLMs)
      • Prompt Engineering
      • AI Agents
      • RAG (Retrieval Augmented Generation)
      • AI Workflow Automation
      • AI Agents vs LLM-based APPs
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  1. Overview of GenAI
  2. Key concepts

Intent Classification

What is Intent Classification?

Intent classification is the process of identifying the purpose or goal behind a user’s input in an AI-driven application. It enables AI agents to determine what the user wants and route the request to the correct workflow or response.

Importance of Intent Classification

  • Helps AI applications understand user queries accurately.

  • Routes the user to the correct process or action.

  • Improves user experience by reducing friction in interactions.

  • Enhances automation by enabling AI to trigger workflows based on intent.

Traditional Challenges

  • Ambiguous User Inputs: Users phrase requests in different ways, making it hard to classify intent correctly.

  • Context Understanding: Simple keyword matching fails when context is required.

  • Handling Edge Cases: Uncommon or out-of-scope queries often misfire or go unclassified.

  • Scalability Issues: Rule-based intent detection struggles with large datasets and complex interactions.

  • Semantic Understanding: Semantic understanding poses a significant challenge in intent classification due to the complexity of human language. It involves interpreting the meaning behind a sentence and identifying the speaker's underlying intention.

How AI Solves These Challenges

  • Machine Learning Models: Use NLP (Natural Language Processing) models trained on varied user inputs to classify intents accurately.

  • Context-Aware Models: Advanced AI models can understand context and infer meaning beyond direct keyword matching.

  • Continuous Learning: AI models improve over time by learning from new data and user interactions.

  • Multi-Intent Recognition: AI can detect multiple intents in a single input, leading to more dynamic responses.

New Possibilities Enabled

  • Dynamic Workflows: AI agents can route users dynamically to different application features.

  • Conversational AI Agents: Chatbots and virtual assistants can handle complex, natural conversations.

  • Better Personalization: AI can adjust responses based on detected user intent and past interactions.

  • Automated Process Execution: AI-driven intent classification enables intelligent automation, reducing manual effort.

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Last updated 4 months ago