Evaluate LLMs and AI systems
You evaluate Large Language Models (LLMs) and entire AI systems in interconnected ways, but they differ in scope, metrics, and complexity.
LLM-specific evaluation focuses on assessing the model's performance on specific tasks like language generation, comprehension, and translation. You use quantitative metrics such as perplexity, BLEU (Bilingual Evaluation Understudy) scores, or human-judged fluency and relevance.
Evaluating entire AI systems involves broader considerations, including how well the LLM integrates with other components, contributes to the system's goals, and impacts user experience.
Start with LLM-specific evaluation
You start LLM evaluation by testing the model's ability to understand and generate language using standard datasets and benchmarks to measure accuracy, fluency, and context maintenance.
These metrics help you understand the model's capabilities in isolation but potentially don't reflect real-world performance. For example, a model might generate coherent text in a controlled environment but struggle in dynamic, user-driven conversations.
Evaluate entire AI systems
When evaluating entire AI systems, you consider the LLM as one component of a larger system. You must evaluate how the model interacts with other subsystems like data retrieval mechanisms, user interfaces, and decision-making algorithms.
The LLM's performance affects the system's effectiveness, but so does how well it integrates and how its outputs are used.
For example, an AI system for customer support might use an LLM to generate responses. The system's success depends on matching responses to user queries and aligning with business goals. The alignment with user queries and business goals potentially depends on the retrieval of relevant context from your customer databases.
Understand the relationship between LLM and AI system evaluation
The relationship between LLM and AI system evaluation depends on the system's overall goals.
LLM evaluation looks at language and cognitive metrics. On the other hand, an entire AI system involves checking if it meets specific goals like improving user satisfaction, automating tasks, or providing useful insights.
You need a comprehensive approach that considers the LLM's technical performance, how well it fits the system's purpose, and your user satisfaction.
Address challenges in evaluating AI systems
Evaluating AI systems also introduces challenges related to ethics, fairness, and bias. While LLM evaluation might identify biases in the model's outputs, system-level evaluation must consider how these biases affect decisions.
For example, an LLM in a hiring system might generate biased recommendations, leading to discriminatory practices. Therefore, system evaluation must address the LLM's technical quality and broader societal implications.
As the LLM evolves, the evaluation criteria for the entire system must also adapt. Improvements in the LLM can necessitate changes in system evaluation, especially if new functionalities or more complex integration are required.
System performance shortcomings might also require you to reevaluate the LLM you're using, leading to further fine-tuning or retraining.
In practice, a combination of quantitative and qualitative methods is used to evaluate LLMs and AI systems. Metrics such as accuracy and precision provide valuable insights into the LLM's performance, while user feedback and real-world testing help understand the AI system's overall functionality. This dual approach ensures that the LLM performs well in isolation and contributes effectively to the system's goals.