As we’ve been building AI agents recently, we’ve spent considerable time reflecting on how to effectively measure the output quality from these large language models (LLMs).
Let’s break down three key observations we’ve made:
1. Measuring AI in Real-World Contexts
Standard benchmarks provided by LLMs, such as MMLU, offer generalized evaluation techniques that help us compare performance across different models. While these benchmarks are useful, they don’t evaluate performance in the context of your specific use cases. Just as an attorney’s competency isn’t solely measured by how high their BAR score is, benchmark results should not be the sole criterion for selecting an LLM. Instead, evaluation should focus on how well the LLM performs in the context of its intended application, considering factors like relevance, accuracy, and effectiveness in addressing the specific needs of the target audience.
2. The Importance of Prompt Quality
When conducting prompt testing, it’s crucial to recognize that you’re not just evaluating the LLM’s performance—the quality of your prompts plays a significant role. If your prompts are poorly crafted, even the most advanced LLMs will produce subpar outputs. To effectively control and test your prompts, consider using a controlled environment to isolate variables, testing prompts across different contexts for consistency, and iteratively refining your prompts based on performance metrics tied to your specific use case.
3. Performance of Your Prompts Will Change
Even after thorough prompt testing, it’s crucial to understand that prompt performance is not static. Several factors can influence how your prompts behave over time. Model updates may introduce new capabilities or alter existing ones, potentially affecting prompt effectiveness. The inherent variability in language model outputs means that even identical prompts can yield slightly different results across interactions. For systems utilizing Retrieval-Augmented Generation (RAG), changes in the retrieved information can lead to variations in responses as well. This dynamic nature of prompt performance underscores the importance of ongoing monitoring and periodic re-evaluation to ensure continued effectiveness and alignment with your objectives.
By following these best practices to measure generative AI output quality, you’ll be better positioned to create AI agents that deliver consistent and relevant results.
Got a tip on evaluating AI agent performance? Let’s hear it. Comment below!
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