Semantic Search
Semantic search is a retrieval technique that finds documents based on meaning rather than exact keyword matches. It works by encoding text — queries and documents alike — into dense vector embeddings using transformer models, then measuring similarity between those vectors. This powers modern RAG systems, enterprise search, recommendation engines, and any AI application that must retrieve contextually relevant information at scale.
In 2026, virtually every production AI application — from customer support chatbots to internal knowledge bases — relies on a retrieval layer, and semantic search is the standard approach. Companies building RAG pipelines, agentic workflows, and multimodal search systems specifically hire engineers who can choose and tune embedding models, build vector indexes, and implement hybrid retrieval strategies. Mastery of semantic search is now a core differentiator between junior and senior AI engineers.
🎓 Courses
Large Language Models with Semantic Search
by Jay Alammar (Cohere)
The most focused course on semantic search specifically — covers keyword search baselines, dense retrieval with embeddings, Cohere Rerank for reranking, and LLM-augmented summarization of results. Free short course, directly applicable.
Semantic Search with FAISS (LLM Course, Chapter 5)
by Hugging Face team
Official hands-on tutorial from Hugging Face showing how to build a semantic search engine using datasets, sentence embeddings, and FAISS indexing. Free, code-first, integrates directly with the HF ecosystem.
Open Source Models with Hugging Face
by Maria Khalusova (Hugging Face)
Teaches how to use Hugging Face Hub models for NLP tasks including text similarity and retrieval — a necessary foundation before diving deeper into vector search pipelines.
Sentence Transformers Official Documentation and Quickstart
by UKP Lab / Hugging Face team
The definitive reference for the de-facto library used in semantic search production systems. Covers bi-encoders, cross-encoders, training, fine-tuning, and 10,000+ pretrained models on the HF Hub.
How to Build a Semantic Search Engine with Transformers and FAISS
by James Briggs
A widely referenced practical tutorial that walks through the full pipeline — embedding generation, FAISS indexing, and query execution — with working code. Good complement to official documentation.
📖 Books
Natural Language and Search: Large Language Models (LLMs) for Semantic Search and Generative AI
Jon Handler, Milind Shyani, Karen Kilroy · 2024
The most directly relevant book on the topic — covers the full spectrum from lexical/keyword search to dense vectors, hybrid search, SPLADE sparse vectors, RAG, and multimodal search. Includes real-world case studies from Walmart and Novartis. Written by AWS engineers deeply embedded in OpenSearch.
Vector Search for Practitioners with Elastic
Bahaaldine Azarmi, Jeff Vestal · 2024
A practitioner-focused book on deploying vector search in production using Elasticsearch/Elastic. Covers HNSW indexing, ANN algorithms, hybrid retrieval, and integration patterns relevant to security and observability teams.
🛠️ Tutorials & Guides
Semantic Search with FAISS — Hugging Face LLM Course Chapter 5
The canonical step-by-step tutorial using the HF datasets library, sentence embeddings, and FAISS to build an asymmetric semantic search engine. Free, maintained, and reproducible in Colab.
A Step-by-Step Guide to Building a Semantic Search Engine with Sentence Transformers, FAISS, and all-MiniLM-L6-v2
A March 2025 hands-on tutorial building a semantic search engine over scientific abstracts using the popular all-MiniLM-L6-v2 model. Concise, code-complete, and covers the full pipeline from install to query.
The Ultimate Guide to FAISS Indexing with Sentence Transformers for Semantic Search
Focuses specifically on FAISS index types (IVF, HNSW, PQ) and how to choose between them based on dataset size and latency requirements — practical guidance missing from most beginner tutorials.
Learning resources last updated: June 18, 2026