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🔗 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.
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