RAG (retrieval-augmented generation) is a pattern in which a language model answers using passages retrieved from your own documents at query time and cites those passages, so the reader can verify. Traditional search returns ranked links and leaves composition to the reader.
RAG systems and traditional search look similar from the outside. The difference is what the user gets back and how much they still have to piece together.
| Dimension | Traditional search | RAG |
|---|---|---|
| Output | Ranked links | Composed answer with citations |
| Coverage of long-tail | Depends on keywords | Broader, semantically matched |
| Freshness | Index-dependent | Index-dependent (retriever) |
| Auditability | Direct source | Direct source + composed reasoning |
| Hallucination risk | None | Present must be measured |
Verdict: Traditional search wins when the user is happy with a link. RAG wins when the user wants an answer and you can invest in the eval loop that makes it trustworthy.
Hallucination risk exists, but is bounded by the retrieval step: the model composes only from the passages supplied. A good RAG system also measures grounded-ness (does every claim trace back to a retrieved passage?) and refuses out-of-scope questions. Weekly evaluation during active engagements keeps the number near zero in practice.
Fine-tuning changes the model’s weights; the knowledge becomes part of the model and is hard to inspect or update. RAG leaves the model unchanged and feeds it current documents at query time. RAG is faster to update, easier to audit (every answer cites its source), and safer for regulated content.
Chunking + metadata strategy, embedding model choice, retriever evaluation (precision at k), prompt orchestration with system constraints, access control so retrieved passages respect per-user permissions, logging + traces for every answer, and a growing eval set measured on real questions.
Yes. We regularly deploy RAG against OpenAI, Anthropic, xAI, Azure OpenAI, Google Vertex, Bedrock, and self-hosted (vLLM, Ollama) models. For German B2B clients with data-residency constraints, EU-hosted or on-prem is the default.
If the user wants to browse a list of documents (research, discovery), traditional search is better. If the question requires reasoning across the whole corpus rather than specific passages, agents + tools often beat RAG alone.
