Build Better LLM Apps with Assertion-Based Unit Tests

Video Description

Want to start freelancing? Let me help: https://academy.datalumina.com/freelance Want to learn real AI Engineering? Go here: https://academy.datalumina.com/accelerator 💼 Need help with a project? Work with me: https://www.datalumina.com/ 🔗 Article https://applied-llms.org/ 🛠️ My Development Workflow https://youtu.be/3sIzCFuLgIQ ⏱️ Timestamps 0:10 Introduction to LLM Evaluation Techniques 2:46 Understanding Data Processing Steps 3:59 Writing Assertions for LLM Outputs 6:39 Structuring Your Evaluation Logic 📌 Description In this video, I discuss practical evaluation techniques for enhancing the reliability of large language model (LLM) applications. I introduce assertion-based unit tests and methods to capture real-world input data, enabling effective analysis of customer interactions. I highlight the importance of structured outputs with the Instructor library and demonstrate how multiple assertions can validate system responses. Additionally, I discuss organizing code for better maintenance and recommend the observability platform Langfuse for tracking API calls. Finally, I share insights on a boilerplate project for event-driven LLM applications and tips for developers transitioning into freelancing.