Build Better LLM Apps with Assertion-Based Unit Tests

Key Concepts

• LLM application reliability
• Assertion-based unit tests
• Real-world input/output samples
• Structured output (Instructor library)
• Evaluation techniques
• Data gathering and analysis
• Confidence intervals
• Escalation logic
• Observability platforms (Langfuse)
• Event-driven LLM applications

Improving LLM App Reliability with Assertion-Based Unit Tests

Introduction

The video focuses on improving the reliability of LLM applications by implementing simple yet effective evaluation techniques using assertion-based unit tests directly within the codebase. These techniques are based on capturing real-world input/output samples and validating the LLM's output against expected results.

Gathering Real-World Data

• The first step is to gather real-world input data samples from the system or source where the data originates.
• Examples include data from a customer care ticketing system (emails with sender, recipient, and content) or user prompts.
• The goal is to understand the types of requests the system will handle.

Processing Data with Structured Output

• The video uses the Instructor library to obtain structured output from LLMs.
• The process involves feeding the input data to the LLM and specifying the desired output format (e.g., a Pydantic model).
• Example: Analyzing a customer support ticket to extract intent, confidence level, and escalation requirements.

Implementing Assertion-Based Unit Tests

• The core technique involves writing assertions to validate the LLM's output against expected values.
• Example:
  • assert response_model.customer_intent == "billing_invoice" (checks if the customer intent is correctly classified).
  • assert not response_model.escalate (checks if the escalation flag is set correctly).
  • assert response_model.confidence > 0.9 (checks if the confidence level is above a certain threshold).
• The assert statement in Python raises an error if the condition is false, indicating a failure in the LLM's output.

Practical Application and Workflow

• The video recommends creating at least three assertions per input sample.
• The evaluation logic should be separated from the main application code (e.g., in an "evals" folder).
• Data examples (input/output pairs) can be stored in JSON files.
• Whenever changes are made to the system (especially prompts), the assertions should be re-run to ensure that the changes haven't introduced regressions.

Scaling and Automation

• The video suggests creating a script to loop over multiple data examples and run the assertions automatically.
• This allows for more comprehensive testing and validation.
• Integrating an observability platform like Langfuse to monitor API calls and traces is also recommended.

Benefits and Advantages

• Early detection of issues in LLM output.
• Increased confidence in the reliability of LLM applications.
• Faster iteration and debugging.
• Improved ability to manage LLM applications at scale.

Example Scenario: Customer Support Automation

• The video mentions a customer support automation tool launched during Black Friday.
• The assertion-based unit tests helped ensure the system's stability and reliability during peak usage.
• The system analyzes incoming tickets, determines the intent, and routes them accordingly.

Instructor Library

• Instructor is used to get structured output from LLMs.
• It allows defining Pydantic models that specify the desired output format.
• This makes it easier to validate and process the LLM's output.

Observability Platform: Langfuse

• Langfuse is an open-source observability platform for monitoring LLM applications.
• It allows tracking API calls, traces, and other relevant metrics.
• This helps identify performance bottlenecks and debug issues.

Data Lumina Launchpad

• The video mentions a Launchpad that provides access to a boilerplate project for building event-driven LLM applications.
• The project includes the codebase, deployment infrastructure, and other resources.

Conclusion

The video provides a practical guide to improving the reliability of LLM applications by using assertion-based unit tests. By capturing real-world data, processing it with structured output, and validating the results with assertions, developers can build more robust and reliable LLM-powered systems. The combination of unit tests, observability platforms, and structured output techniques is essential for managing LLM applications at scale.