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A technical explainer distinguishing semantic, vector, and hybrid search — what each does under the hood, when to use which, and how Azure AI Search implements all three for RAG grounding on the AI-103 exam.
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Ask ten AI-103 candidates to define semantic, vector, and hybrid search and you will get ten overlapping, half-right answers. This is the single most confusable topic on the AI-103 exam, and it shows up across both the generative/agentic and information extraction domains because it underpins RAG grounding. Here is the clear version.
Classic keyword search ranks documents by term overlap, weighted by term frequency and rarity (the BM25 algorithm). It is exact and fast, and it excels at precise tokens — product IDs, error codes, names, SKUs. Its weakness: it does not understand meaning. Search "car" and it will not match "automobile" unless you have synonyms configured. It is the foundation the other approaches build on.
Vector search converts both your query and your documents into embeddings (high-dimensional numeric vectors) and ranks by similarity (typically cosine distance) in that space. Because embeddings capture meaning, "car" and "automobile" land near each other, so vector search handles paraphrases and synonyms well. Its weakness is the mirror image of keyword search: it can miss exact tokens — a specific invoice number or a rare product code may not be well represented in embedding space. For the foundations, see our embeddings & vector search guide.
Semantic search (in Azure AI Search, the semantic ranker) is a reranking step applied on top of an initial result set. It uses a language model to re-score and reorder candidate results by how well they actually answer the query, and it can surface captions and answers. Crucially, semantic ranking does not replace retrieval — it improves the ordering of whatever candidates the retrieval step returned. Think of it as a quality pass, not a retrieval strategy by itself.
Hybrid search runs both keyword (BM25) and vector retrieval and fuses their results (Azure AI Search uses Reciprocal Rank Fusion to merge the two ranked lists). You get keyword precision (exact IDs, rare terms) and meaning-based recall (paraphrases, synonyms) in one query. Add the semantic ranker on top and you have the strongest default for RAG grounding.
| Approach | Retrieval signal | Handles synonyms? | Handles exact IDs/codes? | Extra step |
|---|---|---|---|---|
| Keyword (BM25) | Term overlap + frequency | No (without synonyms) | Yes | — |
| Vector | Embedding similarity | Yes | Weak | Embed query + docs |
| Semantic | Reranks existing candidates | Improves ordering | Improves ordering | LM reranking pass |
| Hybrid | BM25 and vector, fused | Yes | Yes | Rank fusion (RRF) |
The key mental model: keyword and vector are retrieval strategies; semantic is a reranking layer; hybrid is the combination of the two retrieval strategies. Semantic ranking can be layered on any of them, most powerfully on hybrid.
The exam tell: a scenario describing "combining keyword precision with meaning-based relevance" is hybrid — not "semantic alone." Candidates who pick semantic there are conflating the reranking layer with the retrieval combination. Similarly, if a scenario says "users search with synonyms and paraphrases and miss exact-keyword documents," the fix is usually to move from keyword-only to hybrid (or add vector), not to crank up chunk size.
Azure AI Search is the documented retrieval engine for AI-103, and it supports the whole stack in one service:
This ties directly into RAG ingestion: chunk documents, generate embeddings, attach grounding metadata, and index — then ground your Foundry app or agent on hybrid + semantic results. For the ingestion side, see our RAG pipeline design guide.
Vector = meaning by embeddings. Semantic = a language-model reranking pass. Hybrid = keyword and vector fused, and the best RAG default when you add semantic reranking. Azure AI Search does all of it in one engine. Nail this distinction and you neutralize the exam's favorite retrieval trap. Keep going with the full AI-103 exam guide, the certification study guide, and the learning pathway.
Behavior described reflects Azure AI Search as of early 2026; verify current features on Microsoft Learn. explainx.ai is not affiliated with Microsoft.
