AI-generated data

As explained in Effective Conversational AI

A generated test dataset is a collection of evaluation examples produced artificially, often by prompting a generative model to create variations of existing examples.

When representative user utterances are unavailable, AI-generated data provides manufactured test examples for checking whether a classifier understands unseen requests. section 4.2.1

Use generated examples as blind test data: keep them outside the training set, run them through the classifier, and score the predicted intents. section 4.2.1

Interpret the results through:

• Accuracy, for overall correctness. section 4.2.1
• Recall, for identifying the correct intent. section 4.2.1
• Precision, for avoiding incorrect intents. section 4.2.1

Generated versus representative tests

Before deployment, generated test data can help measure understanding when actual user utterances are unavailable. After deployment, blind examples taken from user logs provide the more reliable measurement. section 4.2.1

Figure 4.5 K-fold cross validation and generated test data are suitable for situations where representative data is not available. Once a solution is deployed to production, representative blind test data will produce the most reliable measurement of your classifier’s ability to understand.

Compare k-fold cross-validation and generated test data with representative blind testing across predeployment and production contexts. section 4.2.1

How Effective Conversational AI covers it

How deep it goes

Defines generated testing, blind-set separation, and accuracy, recall, and precision, but defers detailed improvement instructions.

Examples

Uses manufactured test sets, unseen test utterances, and representative utterances pulled from user logs.

What it assumes

Assumes a classification-based chatbot with labeled training examples and an understanding of intent prediction.

What you can do after

Evaluate classifier understanding and identify intents where the model performs well or becomes confused.

How it connects

Connects blind testing, classifier accuracy, recall, precision, k-fold cross-validation, and representative user utterances.

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Related concepts

• synthetic dataincludes · Synthetic data is data generated artificially rather than collected directly from the target process, often to approximate its properties or meet specified requirements.
• data enrichmentincludes · Data enrichment is the process of augmenting existing data with additional attributes, context, relationships, or derived information.
• generative modelinguses · Generative modeling is a machine-learning approach that learns patterns or a data distribution to generate new samples resembling the data.
• AI-generated contentkind of · AI-generated content is text, code, images, audio, video, or other material produced wholly or partly by an artificial-intelligence system.

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