How custom evals boost LLM app consistency and performance

The rise of large language models (LLMs) has made AI application development more accessible to organizations without specialized machine learning expertise, but ensuring consistent performance requires systematic evaluation approaches.

The evaluation challenge: Traditional public benchmarks used to assess LLM capabilities fail to address the specific needs of enterprise applications that require precise performance measurements for particular use cases.

• Public benchmarks like MMLU and MATH measure general capabilities but don’t translate well to specific enterprise applications
• Enterprise applications need custom evaluation methods tailored to their unique requirements and use cases
• Custom evaluations allow organizations to test their entire application framework, including prompts, code, and model interactions

Key components of custom evaluations: Successful implementation of custom evaluations requires three critical elements that work together to ensure reliable testing.

• High-quality test data derived from handwritten examples, end-user interactions, or carefully generated synthetic data
• Well-defined tasks that encompass the complete application framework, including all processing steps and external integrations
• Appropriate scoring functions, either using rule-based heuristics for straightforward criteria or LLM-as-a-judge methods for complex tasks like text generation

LLM-as-a-judge methodology: This advanced evaluation technique leverages language models to assess complex outputs, though it requires careful implementation.

• The approach is based on the principle that validating solutions is easier than generating them
• Successful implementation depends on precise prompt engineering
• The method is particularly useful for evaluating text generation and summarization tasks

Adaptation and maintenance: Custom evaluations enable organizations to maintain consistency as the LLM landscape evolves.

• Organizations can easily test new models against existing evaluation frameworks
• Online scoring systems help track performance against current real-world data
• Regular updates to evaluation sets ensure continued relevance as customer behavior changes

Future implications: The growing importance of custom evaluations reflects a broader shift toward data-driven development practices in AI applications, positioning systematic evaluation as a crucial differentiator for enterprise success in the evolving LLM landscape.