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Interactive 3D/LLM Evaluation Pipeline
LLM Evaluation Pipeline - gpt-4o
Dataset
Load eval dataset
500 items
→
Inference
Run model on inputs
500 items
→
Scoring
Compute metrics
5 items
→
Comparison
vs baseline
500 items
→
Report
Generate eval report
1 items
Select a model and click "Run Eval" to start the evaluation pipeline
Eval Pipeline
Benchmark models against baseline
Select Model
Toggle Metrics
What Each Metric Means
Accuracy: exact match rate.
BLEU-4: n-gram overlap with reference.
ROUGE-L: longest common subsequence.
Human Pref: % of time humans prefer this model.

LLM Evaluation Pipeline - Interactive Visualization

An LLM evaluation pipeline automates quality measurement across model updates. It loads an eval dataset, runs inference on each example, scores outputs with multiple metrics (exact match accuracy, BLEU-4 for n-gram overlap, ROUGE-L for longest common subsequence, human preference rates), compares against a baseline, and produces a regression report. Regression detection flags individual test cases where the new model performed worse than the baseline - the most important signal in production LLM ops.

  • Run the full pipeline: Dataset → Inference → Scoring → Comparison → Report - watch each stage complete in sequence
  • Compare GPT-4o, Claude Sonnet, and Llama 3.1 70B on accuracy, BLEU-4, ROUGE-L, and human preference rate
  • See the regression detector flag test cases where the new model performs worse than baseline - critical for safe deployments
  • Toggle which metrics to include in the evaluation - focus on task-relevant signals and ignore irrelevant ones

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.