Picking the best model for a task from experiment metrics

Selecting the best model for a generative AI application requires comparing candidate models (or prompt/model combinations) against task-appropriate metrics logged during experimentation, rather than relying on a single default measure like accuracy. MLflow's tracking and evaluation capabilities let engineers log parameters, metrics, and outputs for each run so models can be compared side-by-side before choosing one for deployment.

1 · Learn the must-know

• 1Metric choice must match task type: use ROUGE/BLEU or LLM-judged relevance/coherence for summarization and generation tasks, and toxicity/answer relevance/groundedness metrics for RAG and chat use cases rather than generic classification metrics.
• 2MLflow LLM evaluation (mlflow.evaluate) can compute both heuristic metrics (e.g., ROUGE, exact match, token count) and LLM-as-a-judge metrics (e.g., answer correctness, relevance, faithfulness) in a single run, logged alongside latency and cost.
• 3Latency and cost per token/request are critical practical metrics for model selection in production, not just quality scores, since a marginally better-quality model may be unacceptable if it is too slow or expensive.
• 4Comparing runs in the MLflow Experiment UI (or via the API) allows side-by-side inspection of metrics, parameters, and sample outputs across multiple models or prompt versions to make an evidence-based selection.
• 5A model that scores well on one metric (e.g., fluency) may score poorly on another (e.g., faithfulness/groundedness), so the best model choice depends on weighting the metrics most aligned with business/task requirements, not picking the top score on any single metric.
• 6Guardrail and safety metrics (e.g., toxicity, PII detection) should be evaluated alongside quality metrics, since a high-quality but unsafe output disqualifies a model regardless of other scores.

2 · Check your understanding