How deeply does Generative AI in Action cover error analysis?

Examines failure causes and categorizes mistakes as semantic, factual, or linguistic, without specifying a remediation workflow.

Error analysis

As explained in Generative AI in Action

Error analysis is the systematic examination of a model's mistakes to identify their types, causes, and recurring patterns.

Error analysis systematically examines where and why a fine-tuned model underperforms. It reveals recurring mistake patterns and helps direct targeted improvements. section 9.3.2

Categorize model mistakes by type:

Semantic errors: misunderstandings of particular query types or intended meaning. section 9.3.2
Factual inaccuracies: responses that contain incorrect information. section 9.3.2
Language inconsistencies: problems with the quality or consistency of generated language. section 9.3.2

Diagnosing a question-answering model

Reviewing outputs may show that the model repeatedly misunderstands certain queries or generates factual errors. These patterns expose limitations and indicate whether additional data or more sophisticated fine-tuning may be needed. section 9.3.2

How Generative AI in Action covers it

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Also explained in

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Machine Learning System DesignA systematic examination of prediction errors to identify their distributions, causes, patterns, and implications for model improvement.

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Related concepts

hyperparameter tuningcontrasts with · Hyperparameter tuning is the process of selecting or adjusting configurable model or procedure settings and evaluating them against a chosen objective.
use caseuses · A use case is a description of a situation or interaction in which a system, method, or capability is applied to achieve or support a goal or outcome.
residual analysishas part · Residual analysis is the examination of differences between observed and model-predicted values to evaluate model fit, assumptions, and behavior.
learning curvehas part · A learning curve is a representation of how performance or proficiency changes as experience, training progress, or training data increases.
machine learning system designpart of · Machine-learning system design is the practice of architecting, building, and evaluating production software systems whose behavior depends on models, data, and operational components.

This explainer was generated by Manning's knowledge graph from the text of Generative AI in Action. The section links point to the parts of the book it draws on.