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KnowledgeLayer 3Classification & Understanding

Intent Classification

Someone sends a message to your system. It could be a question, a complaint, a request, or just feedback.

Your AI processes it. And routes it to... the wrong department. Again.

Support tickets meant for billing end up with engineering. Feature requests go to accounting. Urgent issues sit in the general inbox.

The AI reads the words but misses the point. It does not understand what the person actually wants.

Before you can route, prioritize, or respond, you need to know what someone is trying to accomplish. That starts with classification.

8 min read
intermediate
Relevant If You're
Systems that receive unstructured requests
AI assistants handling diverse queries
Any workflow that routes based on request type

UNDERSTANDING LAYER - Intent classification happens early, before routing or response generation.

Where This Sits

Category 3.1: Classification & Understanding

3
Layer 3

Understanding & Analysis

Intent ClassificationSentiment AnalysisEntity ExtractionTopic DetectionComplexity ScoringUrgency DetectionAwareness Level Detection
Explore all of Layer 3
What It Is

Understanding what someone actually wants

Intent classification analyzes incoming messages to determine the underlying goal. When someone writes "I cannot log in," the intent might be password_reset, account_locked, or technical_support depending on context. The exact words matter less than what the person is trying to accomplish.

This is different from keyword matching or topic detection. A keyword search for "login" catches the message, but does not tell you whether the person wants help, is reporting a bug, or is requesting a new feature. Intent classification bridges the gap between raw text and actionable understanding.

Intent classification answers: "What does this person want to happen as a result of sending this message?"

The Lego Block Principle

Intent classification solves a universal problem: when you receive a request, you need to understand its purpose before you can respond appropriately. Categorizing by goal (not just topic) enables intelligent action.

The purpose detection pattern:

Incoming communication. Multiple possible meanings. Classify by what the sender wants to happen, not just what they mentioned. The goal determines the response path.

Where else this applies:

Email triage - Receptionist scans messages for purpose: needs response, needs forwarding, needs filing, needs deletion.
Meeting requests - Manager classifies: decision needed, information sharing, brainstorming, or status update. Each gets different handling.
Internal questions - Team lead determines: is this urgent issue, planning question, knowledge request, or feedback? Routes accordingly.
Document review - Reviewer identifies intent: approval needed, revision requested, informational only, or requires discussion.
Interactive: Classify Message Intent

See how messages map to intents

Type a message or click an example to see how intent classification works.

Try it: Click an example or type your own message. Notice how the same topic (like "login") can map to different intents depending on what the person actually wants.
How It Works

Three approaches to classifying intent

Rule-Based Classification

Explicit patterns and keywords

Define patterns that map to intents. If message contains "refund" or "money back," classify as refund_request. Simple, fast, and predictable but struggles with nuance.

Pro: Fast, explainable, no AI costs
Con: Brittle with variations in phrasing

LLM Classification

AI determines intent from context

Send the message to an LLM with a list of possible intents and descriptions. The AI understands nuance, sarcasm, and implied meaning. Flexible but adds latency and cost.

Pro: Handles nuance and context well
Con: Slower, costs money per request

Embedding Classification

Semantic similarity matching

Convert the message to an embedding vector and compare against pre-computed intent embeddings. The closest match wins. Fast inference after setup, handles variations naturally.

Pro: Fast inference, handles synonyms
Con: Requires training data per intent
Connection Explorer

How intent classification fits into request routing

Intent classification sits at the beginning of intelligent request handling. It takes raw text input, determines what the sender wants, and feeds that understanding to routing and escalation logic downstream.

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Embedding Generation
Entity Extraction
Intent Classification
You Are Here
Urgency Detection
Sentiment Analysis
Task Routing
Escalation Logic
Intelligent Handling
Outcome
React Flow
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Foundation
Data Infrastructure
Intelligence
Understanding
Outcome

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Upstream (Requires)

Entity ExtractionEmbedding GenerationAI Generation (Text)

Downstream (Enables)

Task RoutingBranching LogicEscalation Logic
Common Mistakes

What breaks when classification goes wrong

Overlapping intents without priority

Your intents include "billing_question" and "subscription_help" but a message like "Why was I charged twice?" matches both. The system picks randomly or returns the first match, leading to inconsistent routing.

Instead: Define clear hierarchies. Create a primary intent for ambiguous cases or use confidence scores to route uncertain classifications to human review.

Too many granular intents

You create 47 specific intents trying to capture every possible request. Classification accuracy drops because the distinctions are too fine. "password_reset" vs "password_change" vs "password_help" all blur together.

Instead: Start with broad categories (5-10 intents) that map to different actions. Split only when you need different handling. If two intents route to the same place, combine them.

Ignoring multi-intent messages

Someone writes: "I need to update my address and also want to know about your refund policy." Your classifier picks one intent and ignores the other. Half the request goes unhandled.

Instead: Design for multi-intent detection. Return all detected intents with confidence scores. Let downstream routing handle messages that need multiple responses.

What's Next

Now that you understand intent classification

You have learned how to determine what someone is trying to accomplish from their message. The natural next step is learning how to route that request to the right handler.

Recommended Next

Task Routing

Directing classified requests to appropriate handlers or workflows

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