A financial services compliance team is rolling out a Foundry-hosted assistant that answers employee questions about internal trading policies. Auditors require that every answer include a reference back to the specific policy document and section it came from, so a reviewer can verify the response against the source. Which approach should the team configure in Foundry to meet this requirement?

The correct approach is retrieval-augmented grounding with citations enabled. This connects the assistant to the actual policy documents, retrieves the relevant passages for each question, and generates the answer from that retrieved content while attaching citations back to the source document and section, which is exactly what the auditors need to verify each response. Increasing the model's maximum output token limit is incorrect because a larger output token limit only allows longer responses; it has no mechanism for tracking which document a fact came from and does not add citations. Fine-tuning the base model on the trading policy documents is incorrect because fine-tuning bakes patterns from the training documents into the model's weights, producing answers that sound like the policies but carry no traceable link back to a specific document or section, and the underlying policy text can also become outdated without a way to flag it, which is the opposite of an auditable, verifiable answer. Raising the temperature setting is incorrect because temperature controls how much variation appears in wording across generations; increasing it makes answers less consistent and does nothing to connect a response to its source, which works against the auditors' verification requirement. Grounding with retrieval and citations is the feature built specifically for traceable, source-backed answers.