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Grounding dataset

As explained in Building Reliable AI Systems

A collection of authoritative information or evidence used to support or verify system responses.

A grounding dataset is authoritative information used as a benchmark for checking system responses. It should cover the topics the system may address and state facts precisely enough to verify individual claims. section 9.1.1

Organize grounding information so claims can be checked against clear sources:

  • Group information by domain; product details might live in a structured database, while policies might be versioned documents. section 9.1.1
  • Make each fact explicit and traceable to a source, such as a stated return window. section 9.1.1

E-commerce chatbot

A ShopBot grounding dataset can include product specifications, prices and availability; shipping and return policies; and customer-support FAQs. A 30-day return window should appear explicitly so a response claim can be checked. section 9.1.1

How Building Reliable AI Systems covers it

How deep it goes
Defines coverage and traceability requirements, with examples of structured product data and versioned policy documents.
Examples
Uses product specifications, pricing, availability, shipping and return policies, and customer-support FAQs.
What you can do after
Evaluate response claims against explicit, sourced facts organized by domain.
How it connects
Connects grounding data to hallucination measurement, test sets, claim validation, and evaluation metrics.

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.