| RAG (Retrieval-Augmented Generation) | VS | Vector Search |
|---|---|---|
| Retrieves relevant documents and feeds them to an LLM to generate grounded, contextual answers. | Finds semantically similar documents using embedding vectors. Pure retrieval without generation. |
RAG (Retrieval-Augmented Generation)
Pros
- Full answer generation
- Source citations included
- Combines retrieval with reasoning
- Natural language output
Cons
- Depends on retrieval quality
- More complex pipeline
- Higher latency
- LLM costs per query
Vector Search
Pros
- Fast similarity matching
- Lower latency
- No LLM costs
- Great for search and recommendations
Cons
- No answer generation
- Just returns documents
- Requires embedding setup
- Limited to similarity, not reasoning
Verdict
Vector search is the foundation; RAG builds on top of it. Use vector search for discovery and recommendations. Use RAG when users need full answers, not just documents.
When to use which
Use vector search for semantic search, duplicate detection, and recommendation engines. Use RAG for Q&A systems, knowledge bases, and contextual assistants.
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