Automated evaluation metrics in Prompt Engineering / GenAI - Model Pipeline Trace

Model Pipeline - Automated evaluation metrics

This pipeline shows how automated evaluation metrics help us measure how well a machine learning model is doing. It calculates scores like accuracy and loss during training and testing to guide improvements.

Data Flow - 5 Stages

1Raw Data Input

1000 rows x 10 columns→Load dataset with features and labels→1000 rows x 10 columns

Feature1=5.1, Feature2=3.5, ..., Label=1

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2Train/Test Split

1000 rows x 10 columns→Split data into training (80%) and testing (20%) sets→Train: 800 rows x 10 columns, Test: 200 rows x 10 columns

Train sample: Feature1=5.1, Label=1; Test sample: Feature1=6.2, Label=0

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3Model Training

800 rows x 9 feature columns→Train model on features and labels→Trained model

Model learns patterns to predict labels

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4Prediction on Test Set

200 rows x 9 feature columns→Model predicts labels for test data→200 predicted labels

Predicted labels: [1, 0, 1, 1, 0, ...]

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5Automated Evaluation Metrics

200 true labels, 200 predicted labels→Calculate accuracy, precision, recall, and loss→Metric scores (accuracy=0.85, precision=0.83, recall=0.80, loss=0.35)

Accuracy: 85%, Loss: 0.35

Training Trace - Epoch by Epoch

Loss
0.8 |****
0.7 |*** 
0.6 |**  
0.5 |**  
0.4 |*   
0.3 |    
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.75	0.6	Model starts learning with moderate accuracy and high loss
2	0.55	0.7	Loss decreases and accuracy improves as model learns
3	0.45	0.78	Model continues to improve with better predictions
4	0.38	0.82	Loss lowers further and accuracy rises
5	0.35	0.85	Training converges with good accuracy and low loss

Prediction Trace - 4 Layers

Layer 1: Input Features

Layer 2: Model Prediction

Layer 3: Thresholding

Layer 4: Compare with True Label

Model Quiz - 3 Questions

What does a decreasing loss during training indicate?

A The model is forgetting data

B The data is getting worse

C The model is learning and improving

D The training stopped

Key Insight

Automated evaluation metrics give clear numbers to understand how well a model is learning and predicting. Watching loss go down and accuracy go up helps us trust the model's progress.