HumenhukAIGP Study Guide
Module 8: AI Governance Vocabulary

Model mechanics and performance

Reviewed by Victor Humenhuk. Published . Reviewed .

What is inside the model and how its behaviour is measured and goes wrong. Variables live in the data; parameters and weights live in the model - and overfitting (memorising) vs underfitting (too simple) is a classic confusion pair.

This cluster covers what is inside the model and how its behaviour is measured - and how it goes wrong. Start with the parameter family, because the micro-distinction is a planted exam point.

  • Parameters → the internal values a model learns during training, counted in billions for LLMs and a rough proxy for scale.
  • Weights → the learned numeric strengths on connections deciding each input's influence - the core kind of parameter.
  • Inference → the production phase where the trained model applies what it learned to new inputs.
  • Generalization → performing well on new, unseen data, not just the training set.
  • Variance → how sensitive outputs are to fluctuations in the training data; high variance is overfitting territory.
  • Entropy → a measure of uncertainty or randomness, used, e.g., to choose decision-tree splits.
  • Accuracy → the share of outputs that are correct against ground truth.
  • Hallucinations → GenAI output that contradicts the source or is factually wrong while presented as fact.
  • Counterfactual → an explanation showing the minimal input change that would flip the output ("had income been higher, the loan would be approved").
Overfitting vs underfitting

Overfitting → the model memorises the training data, noise included - brilliant in training, poor on new data. Underfitting → too simple to capture the pattern, so it fails on training AND new data. Overfit fails on the new; underfit fails everywhere.

Where things live

Memory hook for the parameter family: variables live in the data; parameters and weights live in the model - weights being the learned connection strengths that make up most of the parameters.

Key terms - quick answers

What is “Parameters”?
Internal values a model learns during training; billions for LLMs, a proxy for scale.
What is “Weights”?
Learned connection strengths deciding each input's influence; the core kind of parameter.
What is “Inference”?
Production phase where a trained model applies what it learned to new inputs.
What is “Generalization”?
A model's ability to perform well on new, unseen data.

Sources and study method

This independent lesson uses active recall, spaced retrieval and scenario practice. Read the full study method.