5 Steps to Build Your Own LLM Classification System

Summiz Holo

Five-step method for LLM classification of customer care tickets, multi-modal classification for text, images, and audio, accessible systems with minimal coding, defining business objectives and extracting metadata, assessing urgency and sentiment with confidence scores

• The video outlines a method to create an LLM classification system in five steps, using customer care tickets as an example.
• The approach discussed can be applied not only to text but also to images and audio classification.
• Classification has become more accessible, allowing individuals to build systems with minimal coding, unlike in the past when it required machine learning expertise.
• The initial example demonstrates the limitations of a basic classification system, including unstructured outputs and lack of a confidence score.
• Step one emphasizes the importance of clearly defining the classification objective within the business context and identifying additional values or metadata to extract.
• Identified objectives for the classification problem include accuracy in categorization, assessing urgency and sentiment, extracting key information, and providing a confidence score for uncertain cases.
• Understanding the business impact of implementing the classification system is crucial, as it relates to reducing response time, improving customer satisfaction, and increasing efficiency.

Automating workforce allocation, leveraging instructor library for structured data, defining Python data models, and utilizing penic for validation in classification systems

• The first step in building a classification system is to optimize workforce allocation by automating routine classifications.
• The second step involves using the instructor library, which helps in obtaining structured data from large language models.
• The instructor library is described as a 'secret weapon' for building reliable large language model applications for production.
• It is essential to define structured data models for classification, sentiment, and urgency using Python's data models or the penic library.
• Enumerations (enums) are used to establish predefined categories that the system can accept, providing validation against invalid inputs.
• The penic model leverages powerful data validation features to define keys such as category, urgency, sentiment, confidence score, and suggested action.
• Validation errors are triggered when inputs do not adhere to the defined constraints, ensuring robustness in the classification system.

Integration of ticket classification models with actionable metadata, human-interpretable error feedback, and robust self-correction mechanisms for enhanced analytics

• Step four integrates everything into a single function, allowing for a response model that accepts a ticket classification data model.
• The patched OpenAI client enables the system to receive a ticket classification object instead of a chat completion.
• Errors from the ticket classification model are easily interpretable by humans, providing valuable feedback for the large language model.
• The Max retries parameter improves system robustness by allowing self-correction based on natural language errors.
• The ticket classification system generates actionable metadata, enabling better routing and handling of customer tickets.
• The system can track categories, frequency, and sentiment over time, facilitating analytics and dashboard creation.
• There are infinite possibilities for data extraction depending on specific use cases, emphasizing the importance of clear objectives.

Contextual categorization, model experimentation, latency reduction, and consistent classification across diverse tasks

• Providing context and defining categories (e.g., urgent, angry) in the system prompt can influence outcomes.
• Experimenting with and expanding data models is essential to obtain more information and implement validations.
• Smaller models can effectively handle simple classification tasks, reducing latency and costs.
• Different models can be used for various tasks within the system, such as simpler models for classification and more sophisticated models for generating replies.
• The classification system can accurately categorize issues (e.g., order issues, account access) even with different models, highlighting that key variables remain consistent.