Marella AI
When a ranked list of documents is enough, when retrieval-augmented generation earns the extra step, and why most enterprise products now do both.
Definition
Enterprise search is a product category; RAG is an architecture pattern. They overlap and are not strict alternatives.
The user needs a known document, record, exact passage or broad result set and should judge relevance directly.
The user needs a supported synthesis across retrieved material and the workflow can inspect citations and handle remaining model uncertainty.
Both approaches depend on document ingestion, metadata, identity, permissions, freshness and retrieval quality. RAG adds generation and therefore more evaluation questions.
Separate find-a-document tasks from answer-a-question tasks. Measure retrieval relevance for both, then score answer support and citations only where generation is involved.
A good platform offers both and lets the workflow choose. Compare the task, not the label.
| Decision factor | Search-first | RAG answer |
|---|---|---|
| Primary output | Ranked documents, records or passages for the user to inspect. | A generated synthesis supplied with retrieved context and, where implemented, source citations. |
| Best fit | Known-item lookup, exploration and high-recall review. | Questions that require synthesis, comparison or drafting across sources. |
| Main failure | Relevant material is missing or ranked poorly. | Retrieval may fail and the generated answer may also distort, omit or overstate the evidence. |
| Evaluation | Relevance, recall, precision, freshness, permissions and user effort. | All search measures plus claim support, citation coverage, no-answer and conflict behaviour. |
| Human action | Open and interpret the source material. | Inspect material citations and decide whether the synthesis is adequate for the task. |
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Bring a mixed question set so the evaluation can show where search, answers or both fit.