⭐️ Decoding OpenAI Evals
Blog post from Portkey
The blog post discusses the use of the OpenAI eval framework to evaluate large language models (LLMs) and optimize their outputs for real-world applications. The framework offers two main types of eval templates: Basic Eval Templates, which use deterministic functions for straightforward comparisons, and Model-Graded Eval Templates, which leverage an LLM's reasoning capabilities for more complex evaluations. It emphasizes incorporating evals into continuous integration and continuous deployment (CI/CD) pipelines to ensure model accuracy and identifies blind spots in real-time. The post provides a detailed guide on creating custom evals, running them, and analyzing the results using the OpenAI evals library, highlighting the importance of separating test and train data to avoid bias. Additionally, it notes the significance of understanding eval logs and using custom completion functions to tailor evaluations to specific needs, encouraging users to explore further resources and examples to deepen their understanding of LLM evaluation processes.
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