Semantic search vs. keyword search: When to use each

Building search feels simple: users type what they need, results come back. But anyone building RAG pipelines knows better. Your demo works perfectly, showing off relevant results every time. Then production hits, and users start complaining that searches miss obvious matches while surfacing irrelevant documents.

The problem isn't how you implemented search, it's that different search scenarios need different approaches. Sometimes you need semantic understanding to connect “database slowdowns” with 'performance optimization”. Other times you need exact matching to find product code "SKU-2847-B" without pulling in similar-looking identifiers.

Production apps need both semantic and keyword search working together. This guide covers what makes each approach work, where they differ, when to use each one, and why production systems combine both into hybrid search.

What is semantic search?

Semantic search is a search method that uses neural networks to understand meaning rather than matching exact words. Instead of looking for exact term matches, the system transforms your text into vector embeddings, which are mathematical representations that capture meaning. This lets you find conceptually related content even when queries and documents share no common words.

Semantic search works through three key stages:

• Neural network generation: Transformer models like BERT create embeddings through specialized architectures.
• Similarity calculations: Cosine similarity compares vectors to measure semantic closeness.
• Relevance ranking: The system scores and orders results by semantic proximity.

Here's how the mechanics work. When you feed an input sequence to the embedding layer, it converts the word tokens into a vector format. Standard BERT models produce dense vectors at high dimensions, creating a rich mathematical representation of your text. These vector embeddings capture semantic similarities in mathematical space, which is why searching for "car repairs" returns documents about "automotive maintenance" and "vehicle servicing" even though they use different words. The concepts sit near each other in embedding space, so the system recognizes them as related.

The system calculates similarity between vectors using cosine similarity, which compares vector direction rather than magnitude. This produces scores from -1 (opposite meaning) to 1 (identical meaning), letting the system rank results by how semantically close they are to your query.

Semantic search delivers better precision and recall than keyword search, which is what we’ll jump into next.

What is keyword search?

Keyword search finds results by matching the exact words you type. The system relies on several core components working together:

• Inverted index structure: Maps every unique term to the documents containing it, like a book's index. Instead of reading every page to find "database," you look up the term and jump straight to the relevant pages.
• Query processing: When you search "database optimization," the system looks up both terms, retrieves their document lists, and finds the intersection, which is docs containing both words.
• BM25 ranking: Scores results using three signals: how often terms appear, how rare those terms are across all docs, and document length adjustments to avoid penalizing longer content.

Let's talk a bit more about how ranking works. Best Match 25 (BM25) uses a probabilistic ranking function with two key parameters. The k1 parameter controls term frequency saturation, which means it limits how much extra credit a document gets for having many instances of a search term. The b parameter handles length normalization, adjusting scores based on document length so shorter documents aren't unfairly penalized against longer ones. These defaults work well across diverse collections, which is why BM25 has become the standard for keyword search ranking.

But before any of this happens, the system processes your text through several stages:

• Tokenization: Breaking text into individual words or terms
• Lowercasing: Converting all text to lowercase for consistent matching
• Stop word removal: Filtering common words like "the" and "is"
• Stemming: Reducing words to base forms so "running," "runs," and "ran" all match queries for "run"

Keyword search excels at speed and determinism. It's fast, predictable, and works great when you need exact matches. But it struggles with synonyms and context, which means searching for "car repairs" won't find documents about "automotive maintenance" unless those exact words appear.

What's the difference between keyword and semantic search?

The fundamental difference comes down to how each method matches queries to documents. Keyword search uses lexical matching, which means it looks for exact words through inverted index structures. Semantic search takes a different path by transforming both queries and documents into dense vector representations in high-dimensional space using models like BERT. This lets semantic search capture meaning rather than just matching keywords.

These architectural differences lead to some practical trade-offs you'll notice in production:

Aspect

Keyword Search

Semantic Search

Matching approach

Lexical matching on exact terms via inverted indexes

Dense vector representations in high-dimensional space

Memory requirements

Minimal with sparse representations

Significant for production catalogs

Latency

Fast for large document collections

Higher latency with BERT models on single CPU core

GPU acceleration

Not applicable

Substantially faster on GPU vs CPU

Failure modes

Misses synonyms and contextual meaning

Misses exact terminology and product codes

Best for

Exact identifiers, Boolean logic, compliance

Natural language queries, conceptual similarity, RAG

The interesting thing about these failure modes is that they're complementary, which is exactly why production systems combine both approaches. Think about what happens when someone searches for "database memory issues" using semantic search. They'll get conceptually related content about "cache eviction policies" and "out of memory errors" even though those exact words don't appear in the query. The system understands the semantic relationship between these concepts.

Now, imagine you flip the scenario. If someone searches for a specific error code like "OOM-2024-047," only keyword search will reliably find documents containing that exact identifier. Semantic search might miss it entirely because error codes don't have semantic meaning in the same way natural language does.

When to use semantic search

Semantic search excels in scenarios requiring natural language understanding, conceptual matching, and cross-language capabilities. Here are some specific use cases where semantic search really shines.

RAG implementations

RAG systems retrieve relevant context before generating responses, which means they need to find semantically similar content rather than exact keyword matches. Semantic search converts both your documents and user queries into vector embeddings, then finds the most relevant chunks based on similarity in embedding space. This ensures your AI systems consistently surface the most contextually relevant information for accurate, grounded responses.

Question-answering systems

Users express queries in natural language rather than using precise technical terms, which creates a gap between how people ask questions and how documentation describes solutions. Semantic search bridges this gap by understanding meaning rather than matching exact words. When someone asks "How do I prevent my Redis instance from running out of memory?", semantic search connects that query to content about memory management, eviction policies, and maxmemory configuration even when those specific terms don't appear in the question.

Multilingual apps

Embedding spaces capture semantic meaning across languages without requiring translation as an intermediate step. Semantic search can match a query in English to a document in Spanish if they're semantically similar, opening up truly multilingual search experiences where users find relevant content regardless of the language it was written in.

Conversational AI interfaces

Multi-turn conversations need to understand not just what the user said in the current message, but how it relates to everything that came before. Semantic search provides the context maintenance and information retrieval capabilities that conversational AI requires, making interactions feel natural rather than robotic through semantic comprehension of conversation flow and intent recognition.

When to use keyword search

Keyword search delivers superior results when precision, determinism, and computational efficiency matter more than semantic understanding. Let's look at where keyword search becomes your best option.

Exact term matching

When you're dealing with unique identifiers like product codes, SKUs, model numbers, or database IDs, you need to find precisely what the user typed because semantic similarity doesn't help. Keyword search matches exact terms through inverted indexes, ensuring you find specific codes, technical terminology, medical codes, and legal citations with the precision that compliance and regulatory requirements demand.

Boolean operations & field filtering

Keyword search gives you precise control through AND/OR/NOT operations, field filtering, phrase matching, and proximity search. You can construct complex queries that express exactly what you need, searching for specific values in specific fields rather than conceptually similar content. This surgical precision is essential when users need exact sequences or terms within specified distances of each other.

Deterministic and reproducible results

BM25 and TF-IDF produce consistent results every single time, unlike neural models that can change with retraining or algorithm updates. This determinism becomes critical for regulatory compliance where you need to prove that identical queries always produce identical results, making keyword search the reliable choice for compliance and debugging scenarios.

Small to medium datasets

Traditional database search handles small to medium datasets effectively without requiring the computational overhead of semantic search infrastructure. When you have a table or list with CRUD operations and want to add full-text search functionality, keyword search means faster implementation, lower infrastructure costs, and easier maintenance for workloads that don't require semantic understanding.

Why modern apps need both

Neither approach solves all search problems alone, which is why production systems combine both methods. This brings us to hybrid search, which combines dense vector search with sparse keyword search to address the fundamental limitations that prevent either approach from handling diverse query types effectively.

To understand how hybrid search works, you need to know about its architectural foundation. The system maintains two parallel indexes that work together. There's a vector index, which typically uses algorithms like HNSW (Hierarchical Navigable Small World) for semantic search, and an inverted index using BM25 for keyword search. Both of these indexes reference the same document collection, which means you're not duplicating your actual content.

When it comes to combining results, hybrid search uses Reciprocal Rank Fusion, or RRF for short. This is a method for merging rankings from different search systems, and it consistently yields better results than any individual system working alone. The process works through structured parallel processing. The system executes keyword and vector searches at the same time, scores results independently using each method's native ranking function, and then applies fusion layer processing to merge those rankings into a single, unified result set.

Build hybrid search with Redis

Production search needs both semantic understanding and exact matching to handle the full range of queries your users throw at it. Semantic search excels when users express intent in natural language, while keyword search delivers precision for exact identifiers and compliance scenarios. The real power comes from hybrid search, which combines both approaches to address their complementary failure modes.

Redis provides production-ready hybrid search through its integrated vector search and full-text search components. This unified platform supports both vector similarity search and full-text search without requiring separate systems, which simplifies your architecture considerably. You get native support for hybrid search that combines vector similarity with structured filters, all through a single unified API.

Redis supports multiple index types to handle different performance requirements. Hierarchical Navigable Small World (HNSW) delivers approximate nearest neighbor search with excellent performance and high accuracy. For use cases demanding perfect accuracy, the FLAT index provides brute-force exact nearest neighbor search. AI framework integrations work out of the box, letting you plug Redis into LangChain, build RAG systems, or create conversational AI without wrestling with complex integration code.

Ready to build hybrid search? Try Redis free or meet the team to discuss your use case.