Enhance Your LLM Evaluations with Jest and LangSmith 🚀

This document is aimed at software engineers looking to maximize their efficiency when evaluating large language model (LLM) applications, leveraging popular JavaScript testing frameworks. Here’s an engaging breakdown of how to run evaluations using Jest and LangSmith.

Elevate Your Evaluation Process 🌟

Evaluating the performance of applications is crucial, especially during continuous updates. Traditional testing methods can feel cumbersome, but the integration of LangSmith with Jest (or Vitest) makes evaluating LLM applications 10 times easier! 🎉 This integration ensures that developers can streamline their workflows while maintaining high-quality standards.

Key Benefits of Integrating Jest with LangSmith

1. Rich Terminal Outputs: While testing, you can enjoy user-friendly terminal outputs that consolidate relevant data to improve the development experience. Terminal outputs are essential for debugging, allowing developers to trace specific runs with ease.

2. Long-term Tracking: Gain visibility of metrics over time within the LangSmith UI. This capability allows teams to monitor their progress and determine if they are consistently improving on key performance indicators (KPIs).

3. Centralized Information Access: Non-technical stakeholders benefit from a centralized location to view evaluation results. Instead of searching through code, everyone can access meaningful data about LLM performance and evaluate results collectively.

4. Feedback Loop: Integration allows you to log granular feedback beyond the simple pass/fail paradigm. This comes in handy for understanding the nuances behind specific evaluations and enhances collaboration across teams.

Example: Evaluating a Marketing Copy Assistant 📝

To illustrate these principles, let’s look at a simple example application that generates marketing copy, designed to work seamlessly with our testing framework. This application sends requests to the GPT-4 Mini model, asking it to act as a marketing expert.

Test Suites Overview

• Test Tweet Suite: Asks the assistant to create a tweet and evaluates:

• Length constraint: Is it 280 bytes or less?

• Marketing score: A value greater than or equal to 5.

• Test LinkedIn Post Suite: Aims to create a longer form post with similar checks:

• Length constraint: Must exceed 280 bytes.

• Marketing score: Should also exceed 5.

Setting Up the Integration ⚙️

Getting started with the Jest and LangSmith integration is a breeze. Here’s how you can proceed:

1. Imports: Replace your standard Jest imports with the LangSmith import to wrap your tests easily.

   // Instead of:
   import { describe, it, expect } from 'jest';

   // Use:
   import { describe, it } from 'langsmith-jest';

2. Reformat Inputs: Ensure inputs are structured in an object. For example:

   const input = {
       content: 'Draft a tweet about LLMs',
       type: 'tweet'
   };

3. Logging Outputs: Instead of merely using expect statements, log detailed feedback with custom keys:

   logFeedback('Response Length', response.length);
   logFeedback('Marketing Score', marketingScore);

4. Wrap Evaluations: To separate traces and ensure clarity, use the wrapEvaluator function while calling your LM judge:

   wrapEvaluator(() => {
       // Evaluation logic here...
   });

Running Tests and Analyzing Results 📊

To visualize your evaluations alongside their associated metrics, run your tests in the terminal. The LangSmith UI will automatically create experiments corresponding to each test suite.

Insights from the LangSmith UI

1. Experiment Tracking: View detailed charts showing comparisons across various experiments. For every instance, you can see:

• Inputs sent to the model
• Outputs generated
• Values for designated feedback keys

1. Separation of Concerns: Using wrapEvaluator allows you to keep traces for your model evaluations and evaluations from your LM judge distinctly organized, enhancing clarity in visualizations and reports.

Practical Tips for Effective Evaluations 🤓

• Share Results: Effectively communicate findings across departments by ensuring that evaluations are accessible from the LangSmith UI, allowing team members to discuss improvements collaboratively.

• Customize Feedback: Don’t forget to define feedback keys in your evaluations. This customizability ensures that the metrics valuable to your stakeholders are tracked and reported.

• Utilize Reference Outputs: When appropriate, include reference outputs to make evaluations even more robust. This is particularly useful when ground truth is needed for comparisons.

• Stay Updated: Regularly check the LangSmith documentation for updates and additional functionalities that can enhance the evaluation process.

Resource Toolbox 📚

Here are some essential resources to help you get started and deepen your understanding:

• How-to-guide on Jest and Vitest Integration: A comprehensive guide for new users.
• LangSmith Testing Tutorials: Step-by-step instructions for setting up your testing environment.

Final Thoughts 💭

Incorporating the LangSmith integration with Jest not only refines your development process but also empowers your entire team to engage with performance metrics effectively. By leveraging these tools, you’re not just enhancing LLM evaluations—you’re paving the way for transparent collaboration and continuous improvement in your applications.

Take advantage of this integration to bring your LLM applications to the forefront of excellence!