RAG vs deterministic execution: check the answer and the action

Start with two questions: what supports the answer, and what authorizes the action? Keeping separate records for each makes the design easier to evaluate.

AWS describes RAG as searching data for relevant information and using it to improve generated responses. LangChain describes workflows with predetermined paths and agents that choose their processes dynamically. These are descriptions of information access and control flow; neither description alone establishes that a business decision is correct. AWS Knowledge Bases · LangChain workflows and agents.

What is the practical difference?

For this comparison, deterministic execution means applying specified logic so the same accepted inputs and relevant context produce the same specified result. This is our working definition; name the boundary and conditions whenever using it.

The table is our evaluation framework, based on the retrieval and workflow distinctions above. Its checks are recommendations, not measured product results.

Does a fixed RAG pipeline guarantee the same answer?

LangChain’s prompt-chaining example passes work through successive model calls in a predefined workflow. Our inference is straightforward: knowing the sequence of calls does not establish that each call returns the same result on a fresh run. Test the generated output separately from the sequence. Workflow definitions and prompt chaining.

For an actual reproducibility claim, record the retrieved passages as well as the query. Our companion guide explains deterministic versus probabilistic AI and the conditions to hold fixed.

What would this look like in an expense workflow?

Hypothetical design, not a product test: an employee submits a receipt for 120 units. The applicable approved policy reimburses eligible expenses up to 100 units. Under those invented conditions, the expected amount is 100.

1. Retrieve the applicable policy. Retain its identifier, effective date and relevant passage. If the applicable version is missing, request clarification.
2. Propose and check the inputs. Extract the amount and category from the receipt, then establish that they match the record and satisfy the eligibility rule. Route an ambiguous category for authorized review.
3. Execute the approved rule. For this example, the rule is the smaller of the eligible amount and the cap. Retain the accepted values and rule version alongside the result.
4. Confirm the action. If the workflow submits a reimbursement, retain the destination response and resulting status. Keep a submission with unknown status unresolved.

RAG addresses the policy-information step. Explicit rules address the calculation and permitted transitions. Our design recommendation is to require evidence at their handoff: the generated statement that an expense is eligible should not, by itself, authorize payment.

The prevention guide expands this sequence into evidence checks, human review and action confirmation.

What does a faithfulness score actually establish?

Ragas defines faithfulness as the fraction of response claims supported by retrieved context. The denominator is the response’s claims, not the retrieved documents or completed business tasks. Ragas metric definition.

That definition leaves a separate question: was the context the right authority? In a hypothetical test, an answer could accurately repeat a superseded expense cap. It would agree with that document while failing the requirement to apply the current policy. This example follows from the metric’s reference boundary; it is not a measured failure of Ragas or a named RAG product.

Use the citation-verification guide for source identity and claim support, and the measurement guide for denominators and unresolved evidence.

What should an acceptance test report?

We recommend three separate results:

• Evidence and answers: applicable-source retrieval, supported/contradicted/unresolved claims, and abstentions, with explicit denominators.
• Execution: repeated fresh-run decisions under declared fixed conditions, plus correctness against approved reference cases.
• Actions and exceptions: confirmed, failed and unresolved outcomes; records showing when the process paused and who authorized its resolution.

Include missing policies, conflicting versions and ambiguous inputs alongside ordinary cases. Describe which layer each failure belongs to so the next change addresses the observed problem.

For a broader assurance claim, start with what “hallucination-free” should cover. This comparison supplies a review structure; the cited documentation is not an independent benchmark of a combined system.