Strategies for LLM Evals (GuideLLM, lm-eval-harness, OpenAI Evals Workshop) — Taylor Jordan Smith
Traditional benchmarks and leaderboards are insufficient for production AI. This summary details a practical, multi-layered evaluation strategy, moving from foundational system performance to factual accuracy and finally to safety and bias, using open-source tools like GuideLLM, lm-eval-harness, and Promptfoo.
Adopting generative AI in production is a complex undertaking fraught with challenges that standard benchmarks and leaderboards fail to address. To build scalable, reliable, and safe AI systems, organizations must move beyond simple accuracy scores and implement a comprehensive, multi-layered evaluation strategy.
The Shortcomings of GenAI in Production
Enterprises face numerous hurdles when deploying large language models (LLMs), which necessitates a rigorous and continuous evaluation process. These challenges include:
- Policy and Legal Risks: Internal policies often restrict the use of specific models, and the risk of legal exposure from model outputs—such as the “glue incident” where an AI overview tool presented satirical content as fact—is significant.
- Bias and Discrimination: Models trained on vast, unfiltered internet data often inherit a Eurocentric and US-based bias, which requires active mitigation to ensure fairness.
- Cost and Performance: Inference at scale is a primary concern. The speaker emphasizes, “No matter how good your model is, if it’s not fast, if it’s not reliable, if it’s not affordable, you’re screwed a little bit from the get-go.” Efficiently managing GPU investments and accurately estimating costs remain major challenges.
- Knowledge Cutoff: The static nature of frontier models means they lack up-to-date information, a limitation often addressed with RAG and agentic systems, which introduce their own evaluation complexities.
- Model Collapse: As models are increasingly trained on synthetic, AI-generated data, they risk “a loss of output diversity” and precision over time, moving further away from human-anchored data.
A Layered Framework for Evaluation
To systematically address these issues, a tiered approach to evaluation, analogous to the software engineering testing pyramid, is essential. This pyramid prioritizes the most fundamental aspects of a system before moving to more nuanced, application-specific qualities.
Level 1: System Performance
This is the foundational layer. Without solid performance, no other feature matters. Key metrics include:
- Latency (Time to first token, inter-token latency)
- Throughput (Requests per second)
- Concurrent User Handling
- GPU Utilization
Tools like vLLM and GuideLLM are crucial here. They allow for benchmarking these metrics under various loads and configurations (e.g., adjusting input/output token lengths to simulate different use cases like chatbots vs. RAG).
Level 2: Factual Accuracy and Formatting
Once performance is stable, the focus shifts to the quality and reliability of the model’s output.
- Factual Accuracy: This can be measured using standard benchmarks like MMLU (Massive Multitask Language Understanding). The hands-on portion of the talk utilized MMLU-Pro within the
lm-eval-harnessframework to assess the model’s knowledge. For custom applications, this involves creating bespoke evaluations based on proprietary, fine-tuned data. - Formatting: This ensures the model consistently produces output in the required format (e.g., JSON) for downstream application use.
Level 3: Safety, Bias, and Custom Evals
This top layer addresses the most complex and application-specific aspects of model behavior.
- Safety and Bias: This involves testing for harmful outputs, political bias, or other undesirable behaviors.
- Custom Evaluations: These are tailored to the specific function of the AI system, such as evaluating the tool-calling capabilities of an agent or the retrieval and generation quality in a RAG system (using tools like Ragas).
The framework Promptfoo was highlighted as a powerful tool for creating these custom, scenario-based tests, particularly for safety evaluations.
Incremental Implementation and CI/CD
An evaluation suite should not be built all at once. The recommended approach is incremental, starting with component-level tests (e.g., evaluating just the retriever in a RAG system) and gradually building towards end-to-end system evaluations.
Ultimately, these evaluation tests must be integrated into a CI/CD pipeline. Just as unit and integration tests are automated in traditional software development, LLM evaluations should be run continuously to monitor for performance regressions, degradation in accuracy, or the emergence of new biases as the model or its surrounding components evolve. This ensures that the AI system remains robust, reliable, and aligned with user expectations in a real-world production environment.