Testing Prompt Engineering-Based LLM Applications

Author(s): Youssef Hosni

Originally published on Towards AI.

Hands-On Prompt Engineering for LLMs Application Development

Once such a system is built, how can you assess its performance? As you deploy it and users interact with it, how can you monitor its effectiveness, identify shortcomings, and continually enhance the quality of its responses?

In this article, we will explore and share best practices for evaluating LLM outputs and provide insights into the experience of building these systems. One key distinction between this approach and traditional supervised machine learning applications is the speed at which you can develop LLM-based applications.

As a result, evaluation methods typically do not begin with a predefined test set; instead, you gradually build a set of test examples as you refine the system.

Testing LLMs vs Testing Supervised Machine Learning ModelsEvaluating LLM Outputs: Best Practices1.1. Incremental Development of Test Sets1.2. Automating Evaluation Metrics1.3. Scaling Up: From Handful to Larger Test Sets1.4. High-Stakes Applications and Rigorous TestingCase Study: Product Recommendation SystemHandling Errors and Refining PromptsRefining Prompts: Version 2Testing and Validating the New PromptAutomating the Testing ProcessFurther Steps: Iterative Tuning and TestingConclusion

In the traditional supervised learning approach, collecting an additional 1,000 test examples when you already have 10,000 labeled examples isn’t too burdensome.

It’s common in this setting to gather a training set, a development set, and a… Read the full blog for free on Medium.

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Published via Towards AI