Use RAG when semantic breadth and fuzzy discovery are the priority. Use deterministic retrieval when the same repository state and query must produce the same inspectable decision results. Engineering authority often benefits from deterministic retrieval because citations and omissions need to be reproducible.
The approaches optimise different things
Retrieval-augmented generation commonly uses semantic representations to find material related to a prompt and place it in model context. It is powerful when wording varies widely and the collection is large or unstructured.
Deterministic retrieval applies explicit parsing, fields, filters and ranking rules. Its value is not that it understands every paraphrase. Its value is that a result can be repeated, tested and explained without another model deciding what is relevant.
Why reproducibility matters for decisions
If an agent cites an accepted decision in a plan, a reviewer should be able to run the same query against the same commit and inspect the same record. Stable behaviour also makes regression tests possible: a known deletion question should continue to retrieve the governing retention decision after the corpus changes.
This is particularly important when an empty result must be treated honestly. A deterministic system can report that no classified decision matched; it does not need to generate a plausible answer to fill the gap.
The trade-off
Deterministic retrieval depends on disciplined records and explicit vocabulary. Poorly titled or untagged decisions may not match a natural-language question. RAG can be more forgiving of loose prose and distant semantic relationships.
A hybrid can be appropriate for exploration, but the boundary should remain visible. Semantic suggestions can help a person find candidates; an authoritative answer should still cite validated records and expose how they were selected.
A decision rule
Choose based on the cost of nondeterminism. For a general documentation assistant, flexible semantic retrieval may be ideal. For a merge gate, compliance explanation or reproducible benchmark, prefer a path whose inputs and outputs can be tested directly.