Question

A financial services team builds a loan pre-qualification assistant in Azure AI Foundry. Certain decisions—such as rejecting applicants below a legally mandated minimum credit score—must be enforced with 100% consistency and be fully auditable, while free-form explanation and customer Q&A should be handled conversationally. The team wants to combine an LLM with a deterministic rules engine. Which orchestration approach best satisfies both the compliance and conversational requirements?

Accepted Answer

Evaluate hard eligibility rules in the deterministic rules engine first, then use the LLM only to generate natural-language explanations and answer follow-up questions about the rules-based decision.

Answer

Route all requests through the LLM and add a detailed system prompt instructing it to always enforce the minimum credit score rule.

Answer

Fine-tune the LLM on historical approval data so it internalizes the credit score threshold and returns compliant decisions directly.

Answer

Lower the model temperature to 0 so the LLM produces deterministic outputs that reliably enforce the credit score rule.