To Nha Notes | March 31, 2025, 11:34 a.m.
Evaluating large language model (LLM) based applications is inherently challenging due to the unique nature of these systems. Unlike traditional software applications, where outputs are deterministic and predictable, LLMs generate outputs that can vary each time they are run, even with the same input. This variability arises from the probabilistic nature of these models, which means there is no single correct output for any given input. Consequently, testing LLM-based applications requires specialized evaluation techniques — known today as ‘evals’ — to ensure they meet performance and reliability standards.
There are a number of reasons AI evals are so important. Broadly speaking, there are four key ways they are valuable:
1. They establish performance standards.
Evaluation helps establish performance standards for LLM systems, guiding the development process by providing directional outcomes for design choices and hyperparameters. By setting benchmarks, developers can measure the effectiveness of different approaches and make informed decisions to enhance the model’s performance.
2. They can help ensure consistent and reliable outputs.
Consistency and reliability are vital for the practical deployment of LLM systems. Regular evaluations help identify and mitigate issues that could lead to unpredictable or erroneous outputs. Ensuring the system produces stable and dependable results builds trust and confidence among users and stakeholders.
3. They provide insight to guide improvement.
Continuous evaluation provides valuable insights into how the LLM system is performing. It highlights areas where the system excels and where it falls short, offering opportunities for targeted improvements. By understanding the strengths and weaknesses of the model, developers can refine and optimize the system for better performance.
4. They enable regression testing.
When changes are made to an LLM system —whether in prompts, design choices or underlying algorithms — regression testing becomes essential. Evaluation ensures that these changes do not deteriorate the quality of the output. It verifies that new updates maintain or enhance the system’s performance, preventing unintended consequences and preserving the integrity of the application.
Evaluating LLM systems can be broadly divided into two categories: pre-deployment evaluations and production evaluations. Each category serves distinct purposes and is crucial at different stages of the development and deployment lifecycle.