RAG & Retrieval
Semantic Search
A search method that finds results based on meaning rather than exact keyword matches.
In depth
A search method that finds results based on meaning rather than exact keyword matches. Semantic search works by converting text into vector embeddings and finding the closest vectors in a database. A search for 'how to fix a broken deployment' will find documents about 'troubleshooting production rollbacks' even though they share no keywords. Semantic search is the retrieval backbone of most RAG systems.
Example
In practice, developers reach for Semantic Search when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover RAG & Retrieval.
FAQ
What is Semantic Search?
A search method that finds results based on meaning rather than exact keyword matches.
Why does Semantic Search matter for AI developers?
Semantic Search sits in the RAG & Retrieval part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Semantic Search?
Developers Digest publishes tutorials and videos that cover RAG & Retrieval topics including Semantic Search. Check the blog and YouTube channel for hands-on walkthroughs.
Related terms
RAG & Retrieval
Hybrid Search
A retrieval strategy that combines keyword-based search (BM25, TF-IDF) with semantic vector search (embeddings) to get the best of both approaches.
RAG & Retrieval
Embeddings
Numerical vector representations of text that capture semantic meaning.
RAG & Retrieval
Retrieval
The process of finding relevant documents, passages, or data from a knowledge base in response to a query.
Get Smarter About AI Dev
New tutorials, open-source projects, and deep dives on coding agents - delivered weekly.
One email per weekReal code, not theoryFree forever