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LLM Evaluation doesn't need to be complicated

philschmid.de · 2,196 words · saved by 2 readers

Generative AI and large language models (LLMs) like GPT-4, Llama, and Claude have pathed a new era of AI-driven applications and use cases. However, evaluating LLMs can often feel daunting or confusing with many complex libraries and methodologies, It can easily get overwhelming. LLM Evaluation doesn't need to be complicated. You don't need complex pipelines, databases or infrastructure components to get started building an effective evaluation pipeline. A great example of this comes from Discord, which built a chatbot for 20M users. Discord focused on implementing evaluations that were easy to run and quick to implement. One clever technique they used was to check if a message was all lowercase to determine if the chatbot was being used casually or in another way. In this blog post, we will learn how to set up a simplified evaluation workflow for your LLM applications. Inspired by G-EVAL and Self-Rewarding Language Models, we will use an additive score, chain-of-thought (CoT), and for

LLM Evaluation doesn't need to be complicated July 11, 2024 10 minute read View Code Generative AI and large language models (LLMs) like GPT-4, Llama, and Claude have pathed a new era of AI-driven applications and use cases. However, evaluating LLMs can often feel daunting or confusing with many complex libraries and methodologies, It can easily get overwhelming. LLM Evaluation doesn't need to be complicated. You don't need complex pipelines, databases or infrastructure components to get started building an effective evaluation pipeline. A great example of this comes from Discord, which built

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