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From a system design perspective, it's really important to think about your classification problem, consider where it fits into the business context, and also identify what other types of values, metadata, or keywords you potentially want to extract from the data to overall improve the system.
Key Ideas
🎯 Define system goals like accuracy, sentiment analysis, and key information extraction to optimize workforce allocation and improve business outcomes.
📊 Use Pydantic to create structured data models, ensuring input validation for categories and confidence scores. Enums enforce valid inputs.
🤖 Leverage the Instructor library for structured data extraction and integrate with OpenAI for generating structured responses.
⚙️ Implement error handling with Max retries to enhance system robustness and self-correction.
📝 Optimize prompts and data models to improve system performance and extract different levels of information.
📈 Track ticket categories, frequency, and sentiment over time to monitor performance via dashboards.
⚡ Use smaller models for simple tasks to reduce latency and costs, while integrating into larger AI workflows for complex issues.
📃 Video Summary
TL;DR
💨 The video explains 5 steps to build a custom LLM classification system, focusing on customer care tickets. It covers how to automate routine classifications and use Python's Pydantic library for structured data models.
The integration of OpenAI allows for structured responses. The system can classify tickets by urgency, sentiment, and suggested actions, while tracking analytics over time. Experimenting with prompts and smaller models can improve performance and reduce costs.
Defining Clear Objectives for Your Classification System
🎯 Before diving into code, clarify your system's goals. For this example, the objectives include:
- Categorizing tickets accurately.
- Assessing urgency and sentiment.
- Extracting key information for faster resolution.
- Providing a confidence score to flag uncertain cases for human review.
These objectives directly impact business outcomes by reducing response times, improving customer satisfaction, and optimizing workforce allocation.
"We want to reduce the average response time by routing tickets to the right department and improve customer satisfaction by prioritizing urgent and negative sentiment tickets."
Enforcing Input Validation with Pydantic Models
📊 Using Pydantic ensures that your system only accepts valid inputs. By defining enums for categories like "order issue" or "account access," you prevent invalid data from entering the system. If a category like "General" is attempted, the system throws a value error, ensuring consistency.
Additionally, Pydantic models validate other fields like confidence scores, which must be between 0 and 1. This structured approach guarantees that only valid data is processed.
Leveraging the Instructor Library for Structured Responses
🤖 The Instructor library is key to extracting structured data from large language models. By patching the OpenAI client with Instructor, you can request structured responses in formats like JSON, making it easier to integrate into automated systems.
This approach allows you to define exactly what data you want back—whether it's ticket categories, urgency levels, or sentiment—ensuring that the output is both structured and validated.
Enhancing Robustness with Max Retries
⚙️ To make your system more robust, implement Max retries. If the model returns an invalid response, the system automatically retries with feedback from the error message. This self-correcting mechanism ensures that even if the model initially fails, it can adjust and provide a valid response after a few attempts.
This feature is particularly useful when dealing with complex queries where initial responses might not meet validation criteria.
Optimizing Prompts and Data Models for Better Performance
📝 Experimenting with prompts and data models can significantly improve system performance. You can refine prompts to provide more context, such as defining what constitutes "urgent" or "angry" for your specific use case. Additionally, expanding or tweaking data models allows you to extract more or less information depending on your needs.
This flexibility ensures that your system can adapt to different scenarios and extract the most relevant data.
Tracking Metrics Over Time for Continuous Improvement
📈 Once your classification system is up and running, track key metrics like ticket categories, frequency, and sentiment over time. This data can be visualized in dashboards, allowing you to monitor performance and identify trends. For example, you could track how often certain categories appear or how sentiment shifts over time.
This continuous monitoring helps in refining both the system and business processes.
Using Smaller Models for Simple Tasks to Save Costs
⚡ For simpler tasks like basic classification, consider using smaller models like GPT-3.5 Turbo instead of more powerful ones like GPT-4. This reduces both latency and costs while still delivering accurate results for straightforward queries. Reserve more complex models for tasks that require deeper analysis or generation of responses.
Conclusion
🌚 By defining clear objectives and categories (e.g., urgency, sentiment), you can create a robust classification system that automates simple tasks and routes complex issues to humans.
Using tools like Pydantic and OpenAI ensures structured outputs and effective error handling. The system enhances customer care efficiency by providing actionable insights and tracking performance over time.