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Adversarial test set

As explained in Building Reliable AI Systems

A collection of challenging or ambiguous inquiries designed to expose likely model errors such as hallucinations.

An adversarial test set contains challenging or ambiguous inquiries likely to trigger hallucinations. It complements a generic test set of common customer questions, probing failure cases rather than mirroring typical production traffic. section 9.1.1

Build the set with your team around inquiries that could lead an agent astray:

  • Complex product comparisons that may prompt invented features. section 9.1.1
  • Edge cases involving discontinued products or limited-time offers. section 9.1.1
  • Questions with multiple valid interpretations or requests combining multiple topics. section 9.1.1

Use the inquiries to evaluate the system, then validate factual claims in its responses against grounding data. Grounding facts need clear sources so claims can be checked precisely. section 9.1.1

How Building Reliable AI Systems covers it

How deep it goes
Specifies team-led selection of four failure-prone inquiry types and places testing before claim validation.
Examples
Uses product comparisons, discontinued items, limited-time offers, ambiguous questions, and multi-topic requests.
What it assumes
Requires grounding data with clearly sourced facts for precise claim checking.
What you can do after
Evaluate hallucination susceptibility by testing challenging inquiries and checking response claims against grounding data.
How it connects
Connects to generic test sets, grounding data, claim extraction, and hallucination measurement.

Related concepts

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