Overview

The LLM Classifier Skill enables zero-shot or few-shot intent labeling with structured JSON output and confidence scores. It supports multi-label classification, customizable label descriptions, and ensemble patterns, making it suitable for flexible dialogue routing and intent detection in complex conversations.

How This Skill Works

It constructs prompts in zero-shot mode (no examples) or few-shot mode (with labeled examples), optionally using chain-of-thought and ensemble prompts. Outputs are a JSON schema of labels with optional confidence scores, enabling reliable downstream routing and decision logic.

When to Use It

• Designing an intent classifier for a conversational agent with a flexible or expanding taxonomy.
• Need multi-label classification where a single utterance maps to multiple intents (e.g., greeting + product inquiry).
• Calibrating confidence scores to determine when to trigger automated responses vs. human review.
• Comparing different LLM providers or prompts by enforcing a consistent output format across models.
• Designing a dialogue flow that relies on labeled intents and their descriptions for routing decisions.

Quick Start

1. Step 1: Define a taxonomy with clear label descriptions and decide if multi-label is required.
2. Step 2: Choose zero-shot or few-shot pattern, craft prompts, and specify the JSON output and confidence fields.
3. Step 3: Run with LangChain-core, test on edge cases, calibrate confidence, and iterate on prompts and labels.

Best Practices

• Create clear, unambiguous label descriptions to improve consistency across prompts.
• Use representative examples that cover common and edge-case phrases for robust few-shot prompts.
• Maintain a consistent JSON output format to simplify downstream parsing and routing.
• Calibrate confidence scores and set actionable thresholds for automation vs. escalation.
• Test with edge cases and conflicting intents to ensure reliable disambiguation.

Example Use Cases

• E-commerce chatbot routes: classify as 'order-status', 'returns', or 'shipping-info' to direct to the right workflow.
• Multi-label utterance: an input is tagged with both 'greeting' and 'product-question' for contextual handling.
• Edge-case classification: user mentions 'refund' in a complaint—classify with high confidence and escalate if needed.
• Ensemble scenario: combine prompts from multiple models to stabilize frequent intents like 'billing' or 'tech-support'.
• Confidence-calibrated routing: low-confidence intents trigger human review while high-confidence intents proceed automatically.

Frequently Asked Questions