Evaluate LLMs and AI systems
Evaluating Large Language Models and entire AI systems requires different approaches that build on each other.
LLM-specific evaluation assesses the model's performance on language tasks like generation, comprehension, and translation using specialized metrics designed for language quality.
AI system evaluation examines how well the LLM integrates with other components, contributes to system goals, and impacts the user experience in production environments.
Start with LLM-specific evaluation
When you evaluate an LLM, you start by focusing on what the model does: understanding and generating language. This means testing the model's capabilities using standard datasets and benchmarks to measure accuracy, fluency, and how well it maintains context.
These evaluations give you insights into the model's core capabilities, but they don't tell the whole story. A model might generate coherent text in benchmark tests but struggle when faced with dynamic, user-driven conversations or domain-specific requirements that differ from its training data.
Azure Databricks provides MLflow integration for tracking these LLM evaluation metrics, allowing you to compare model performance across different versions and configurations systematically.
Evaluate entire AI systems
AI system evaluation considers the LLM as one component within a larger architecture. You evaluate how the model interacts with other subsystems like data retrieval mechanisms, user interfaces, and decision-making algorithms.
The LLM's performance affects system effectiveness, but integration quality and output utilization are equally important. For example, an AI customer support system using an LLM depends on retrieving relevant context from customer databases, matching responses to queries, and aligning with business objectives.
Address challenges in evaluating AI systems
When you evaluate AI systems, you face additional challenges around ethics, fairness, and bias. While LLM evaluation might catch biases in the model's outputs, system-level evaluation needs to consider how those biases play out in real-world decisions and outcomes.
As LLMs evolve, your evaluation approach for the whole system needs to evolve too. Model improvements might mean you need to update how you evaluate the system, especially when you're adding new features or more complex integrations.
System performance shortcomings might also require you to reevaluate the LLM you're using, leading to further fine-tuning or retraining.
Evaluation approaches combine accuracy and precision metrics with qualitative measures like user feedback and real-world testing. This combination helps ensure that your LLM works well on its own and contributes to your system's goals.