A recent engineering shortlist highlights nine tools designed to evaluate Large Language Models (LLMs) in production environments. These tools, including DeepEval, Ragas, Promptfoo, MLflow, Langfuse, Arize Phoenix, TruLens, Inspect AI, and EleutherAI's LM Evaluation Harness, address various aspects of LLM reliability beyond simple answer correctness. They aim to measure correctness, retrieval quality, tool execution, safety, latency, and cost, recognizing that LLM applications require more nuanced testing than traditional software due to the non-deterministic nature of model outputs. The selection of a tool depends on whether the focus is on model-level capabilities, specific components like retrieval or agent actions, end-to-end application performance, or real-world production behavior. AI
IMPACT Provides engineers with a curated list of tools to ensure the reliability and safety of LLM applications in production.
RANK_REASON Article lists and describes multiple tools for LLM evaluation.
- Arize Phoenix
- DeepEval
- EleutherAI
- Inspect AI
- Langfuse
- LM Evaluation Harness
- mlflow
- Promptfoo
- Ragas
- TruLens
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