5 Steps to Build Your Own LLM Classification System

Summiz Holo

Custom LLM classification for customer care tickets and data types

• The video outlines a method to create a custom LLM classification system in five steps, specifically for classifying customer care tickets.
• Classification can be applied to various data types, including text, images, and audio, using similar principles.
• The traditional complexity of classification systems has been reduced, allowing individuals to build them with minimal coding.
• The initial example demonstrates the limitations of a basic classification approach, including lack of structured outputs and no confidence scoring.
• Step one emphasizes the importance of clearly defining objectives for the classification system, including accuracy, urgency assessment, sentiment analysis, and key information extraction.
• Understanding the business impact of the classification system is crucial for improving response times, customer satisfaction, and agent efficiency.

Optimizing workforce allocation and structured data validation with Pydantic

• The first step in building a classification system is optimizing workforce allocation by automating routine classifications.
• The instructor library is a crucial tool for creating reliable large language model applications, enabling structured data extraction from these models.
• The use of Python's Pydantic library allows for the definition of structured data models, which aids in validation and clarity of objectives.
• Enumerations (enums) are used to define predefined categories for the classification system, ensuring only valid inputs are accepted.
• Pydantic models leverage data validation features to enforce constraints on inputs, such as confidence scores and ticket categories.

Integrating OpenAI client with structured ticket classification and analytics tracking

• Step four involves integrating the OpenAI client with a response model that accepts a ticket classification data model, allowing for structured responses instead of generic chat completions.
• The use of the Penic data model ensures that any input not conforming to the specified format will trigger an error, which is easily interpretable in natural language.
• The Max retries parameter allows the system to self-correct by feeding errors back to the API, enhancing robustness with minimal retries.
• The classification system can categorize tickets based on urgency, sentiment, and suggested actions, providing valuable metadata for customer care teams.
• The system enables analytics tracking for categories, frequency, and sentiment over time, facilitating the creation of dashboards for performance monitoring.
• The importance of clearly defined objectives is emphasized, as it opens up numerous possibilities for data extraction and system expansion.
• Step five focuses on optimizing prompts and experimenting with different prompting structures to improve system performance.

Contextual classification, model experimentation, and AI integration for responses

• Defining specific categories (e.g., urgent, angry) is essential for providing context in a classification system.
• Experimenting with different data models can yield varying amounts of information and validation in the classification process.
• Smaller models can effectively handle simple classification tasks, reducing latency and costs.
• The classification system can be integrated into a larger AI system that automates responses for simple inquiries while routing complex issues to humans.
• Different models may be used for classification and response generation to optimize overall system performance.