Retrieval-Augmented Generation (RAG) has been a game-changer in making large language models (LLMs) more useful and accurate. By grounding responses in external knowledge, RAG helps LLMs stay factual and relevant.
But here’s the ongoing debate: should you use a search engine or a vector database (vector DB) to power retrieval?
At first glance, vector DBs seem like a natural fit. They’re designed for fast, semantic similarity searches, making them great for finding conceptually relevant documents. But if you dig deeper, search engines—especially those designed for relevance-driven information retrieval—are far superior for RAG use cases. Here’s why.
1. Relevance Beats Similarity
Vector DBs primarily rely on embedding similarity, which is great for finding “things like this” but not necessarily for finding “the most relevant thing.” In a RAG workflow, you don’t just want related content—you want the right content.
Search engines combine multiple signals beyond embeddings: keyword matching, recency, metadata, personalization, and ranking algorithms. This multi-faceted approach ensures that results are not just similar but actually useful for answering a given query.
Relevant viewing: The Best Retrieval Method for Your RAG & LLM Applications
2. Hybrid Search is a Must-Have
A pure vector search approach often fails when exact matches matter. Think about looking up specific legal clauses, medical records, or product specifications. Sometimes, a keyword-based search is the only way to ensure precision.
Search engines excel at hybrid retrieval, combining lexical (BM25, TFIDF, keyword-based) and semantic search (vector-based) into a single ranking algorithm. This means you get the best of both worlds—precision when needed and broader recall when similarity helps.
3. Search Engines Handle Scale and Freshness Better
Vector DBs struggle with dynamic and high-scale datasets. Keeping embeddings up to date requires frequent reprocessing, and indexing millions of documents efficiently is a challenge that can be quite expensive to solve. Meanwhile, search engines have long mastered large-scale indexing and real-time updates, ensuring you get fresh and relevant content with far less overhead.
For example, if your RAG system needs to pull the latest customer tickets, financial reports, or breaking news, a search engine can index and retrieve them immediately. Vector DBs? Not so much.
4. Ranking and Context Matter
One of the biggest flaws of vector DBs is that they retrieve content based on similarity alone, without strong ranking mechanisms. This means you might get relevant but unhelpful results—think: multiple snippets saying the same thing, or contextually similar but off-topic documents.
Search engines, on the other hand, use advanced ranking models that consider document quality, user behavior, business rules, and domain-specific relevance. This ensures that your RAG system retrieves not just any relevant documents, but the best ones to ground an LLM’s response.
Relevant viewing: Automatic Relevance Tuning AI Model Overview Demo
5. Search Engines Provide Explainability and Control
With search engines, you get full transparency into why a document was retrieved—via query logs, ranking scores, and tunable parameters. You can tweak ranking rules, boost certain content types, or apply business logic to fine-tune results.
Vector search? It’s often a black box. If your RAG system starts pulling low-quality responses, debugging the root cause in a vector DB can be frustratingly opaque.
The Bottom Line
Vector DBs are great for similarity search, but when it comes to powering RAG applications, search engines provide superior relevance, ranking, scalability, and control. If your goal is to retrieve the most useful information for LLM grounding—not just vaguely related content—then search is the clear winner.
That’s why companies serious about production-grade RAG are moving beyond vector-only approaches and embracing search engines as the backbone of retrieval. If you’re building RAG for real-world applications, make sure you’re using the right tool for the job.
What do you think? Have you experienced the pitfalls of vector search in RAG use cases? Connect with me on LinkedIn, or reach out so we can discuss!