Build Your Own LLM Classification System in 5 Steps
In this video, Dave Ebbelaar walks through the process of creating a large language model (LLM) classification system in five steps. The goal is to classify customer support tickets into categories, assess urgency and sentiment, extract key information, and provide a confidence score for human review. Dave uses Python and the OpenAI library, leveraging the Instructor library to ensure structured outputs and validation.
The five steps include:
1. **Get Clear on Your Objective**: Define the goals and business impact of the classification system.
2. **Use the Instructor Library**: Install and patch the OpenAI client with Instructor to handle structured data.
3. **Define Data Models**: Create structured data models using Pydantic and Enums for categories, sentiment, and urgency.
4. **Integrate and Validate**: Combine the models with OpenAI to ensure robust and validated outputs.
5. **Optimize and Experiment**: Refine prompts, models, and configurations to improve performance and cost-efficiency.
Dave demonstrates how to handle validation errors, ensuring the system only accepts predefined categories and values. He also discusses the importance of optimizing prompts and experimenting with different models to balance cost and performance. The video provides practical insights into building a scalable and reliable LLM classification system suitable for production use.