Semantic search explained: AI, LLMs, embeddings, history, RAG, Elasticsearch, Copilot

Semantic search finds results by meaning and intent rather than exact keywords, so a question about fixing a slow laptop can match an article on improving computer performance. It underpins modern search engines, RAG and enterprise assistants. RAG.NextUpgrad combines semantic retrieval with BM25 keyword search and reranking, so this page covers what semantic search means, whether it needs AI, LLMs or embeddings, its history and importance, its role in RAG, Elasticsearch and Copilot.

What is meant by semantic search?

Semantic search is search that understands the meaning and intent behind a query, returning relevant results even when they use different words. It contrasts with lexical search, which matches exact terms.

For example, searching for family cars with lots of space can return pages about minivans, even if they never use those exact words. Semantic search handles synonyms, paraphrases, natural language questions and sometimes different languages. Most modern implementations convert queries and documents into embeddings and compare them by similarity. Production systems usually combine semantic and keyword search, since exact matches still matter for names, codes and technical terms. Search engines, e-commerce sites and help centres all use it today.

Does semantic search require AI?

In practice, modern semantic search relies on AI models that produce embeddings. Earlier approaches used synonym dictionaries, thesauri, knowledge graphs and statistical methods such as latent semantic analysis, which captured some meaning without modern AI.

Those older methods were brittle and required manual curation, while neural embedding models learn meaning from large text collections and generalise far better. Today, small open embedding models run cheaply on ordinary servers or even in browsers, so adding semantic search no longer requires large infrastructure. Start with a pretrained model and evaluate before fine-tuning. If your queries are mostly exact identifiers, keyword search may be enough; semantic search adds most value for natural language questions.

Does semantic search require an LLM?

No. Semantic search needs an embedding model, which is usually much smaller and faster than a large language model. LLMs are optional extras, used to rewrite queries, generate answers from results or rerank in some systems.

Also asked as: does semantic search require llm

Embedding models such as BGE, E5 or commercial embedding APIs convert text into vectors cheaply, and vector search finds similar items in milliseconds. Adding an LLM turns search into question answering, as in RAG, but increases cost and latency. Many applications, such as product search, document discovery or duplicate detection, work perfectly with embeddings alone, which keeps costs predictable as usage grows. Use an LLM only where generated answers or deeper query understanding add clear value.

Does semantic search use embeddings?

Most modern semantic search does. Queries and documents become embedding vectors, and the system retrieves documents whose vectors are closest to the query's, typically by cosine similarity, using vector indexes for speed.

Other semantic approaches exist: learned sparse models such as SPLADE and Elastic's ELSER expand terms with related words while using inverted indexes, and knowledge graphs connect entities and relationships. Hybrid systems combine dense embeddings with keyword or sparse signals for robust results. Embedding quality, chunking and metadata filters strongly influence relevance, so evaluate on real queries and store the model version alongside vectors. Rerankers can then refine the top results with deeper comparison of query and text.

Is semantic search machine learning?

Modern semantic search is built on machine learning: embedding models are neural networks trained on large datasets to place related texts close together. The search itself then uses those learned representations with vector similarity.

Retrieval steps, such as nearest neighbour search, are algorithms rather than learning, but they depend on learned embeddings for meaning. Some systems add learning-to-rank models that combine signals such as relevance, freshness and popularity. Fine-tuning embedding models on your domain data, such as legal or medical queries, can improve results further. Evaluation metrics such as recall and nDCG measure how well it works. Semantic search is best described as an application of machine learning to information retrieval.

When was semantic search invented?

Ideas behind semantic search date to the 1990s, with latent semantic analysis in 1990 and the Semantic Web vision in 2001. Web search became noticeably semantic with Google's Hummingbird update in 2013, RankBrain in 2015 and BERT in 2019.

Google's Knowledge Graph, launched in 2012, helped search understand entities rather than strings. Dense retrieval with neural embeddings matured from around 2019 to 2020, with Sentence-BERT and dense passage retrieval making semantic search practical for developers. Vector databases then made it easy to deploy at scale. Each step moved search from matching strings toward understanding meaning. Today, semantic search is standard in web search, e-commerce, enterprise tools and RAG systems, usually combined with traditional keyword ranking.

Why is semantic search important?

People describe the same need in many different words, and keyword search misses relevant results that use different vocabulary. Semantic search understands intent, improving relevance for natural language questions, synonyms and multilingual content.

Also asked as: can elastic search do semantic search

Better search saves time, increases sales in e-commerce, reduces support tickets when help articles are findable, and improves employee productivity in enterprise knowledge bases. For AI applications, semantic search is the foundation of RAG, supplying the right context so models answer accurately with citations. Combined with keyword search, it handles both vague questions and exact lookups. Poor retrieval limits even the best language model, so semantic search quality directly shapes AI answer quality.

How does semantic search work in RAG?

In RAG, semantic search is the retrieval step: documents are chunked and embedded in advance, the user's question is embedded, the most similar chunks are retrieved, and those passages are given to the language model as context for its answer.

Quality improves with hybrid retrieval, combining semantic results with BM25 keyword results through methods such as reciprocal rank fusion, and with reranking to order the best passages first. RAG.NextUpgrad follows this pattern and adds a confidence gate that declines to answer when retrieved evidence is weak. Metadata filters restrict results to relevant documents or permissions. Evaluate retrieval separately from generation, measuring whether the right passages appear in the top results.

Can Elasticsearch do semantic search?

Yes. Elasticsearch supports semantic search through dense vector fields with approximate kNN search, the semantic_text field type that handles embeddings automatically, and Elastic's ELSER learned sparse model. Hybrid queries combine semantic and BM25 results.

You can use built-in models, deploy your own, or connect external inference services for embeddings. Reciprocal rank fusion merges keyword and semantic rankings without manual score tuning. For teams already running Elasticsearch, adding semantic search avoids introducing a separate vector database, keeping security, filtering and operations in one place. OpenSearch offers similar capabilities through its neural search features. Test relevance on representative queries, tune chunk sizes before rollout, and monitor query latency as vector fields grow.

What is semantic search in Copilot?

Microsoft 365 Copilot uses a semantic index of your organisation's content, such as emails, files, chats and meetings, to find information by meaning and context, respecting each user's existing permissions. This helps Copilot ground answers in relevant work data.

For example, asking Copilot about last quarter's project risks can surface relevant documents and messages even if they never use those exact words. Windows also offers semantic search on some Copilot+ PCs, letting you find files and settings by describing them in natural language. As with any AI search, results depend on data quality and permissions, so organisations should review access settings before rollout to avoid exposing sensitive information.

Pranjul Rathour

GenAI Engineer · India

GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).

Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.

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