How would you design a semantic search system that finds relevant documents using embeddings?

Imagine a search engine that understands what you mean, not just what you type. That’s the idea behind semantic search. It looks beyond exact keywords and tries to grasp the intent and context of your query, returning relevant results even when the exact words don’t match. Semantic search matters because it makes finding information easier and more accurate. It powers natural language search features in everything from web search engines to e-commerce sites, helping users get better answers.

Whether you’re doing mock interview practice or gearing up for a technical interview, understanding semantic search can be a valuable part of your system design toolkit. It’s a modern system architecture concept that often appears in technical interview tips for AI and system design roles. This article will break down what semantic search is, how embeddings make it possible, and how to design a semantic search system step by step.

What is Semantic Search?

Semantic search is a search technique that focuses on the meaning of the query rather than just matching keywords. Instead of literally matching words, a semantic search engine interprets the intent behind your words and the context around them. In practice, this means it can handle synonyms, rephrased queries, and even slight spelling errors to figure out what you’re really looking for. It uses AI and natural language processing to capture nuances in language – understanding that “cheap phone with a good camera” means the same as “affordable phone with a quality camera,” for example. Traditional keyword search would treat those as different queries, but semantic search knows they’re asking for the same thing.

To see the difference, consider a simple example: A user searches for “increase text size on display.” A basic keyword-based engine might only look for pages containing the words “increase,” “text size,” and “display,” and it could miss an article titled “How to adjust font size in settings” because the wording is different. A semantic search engine will recognize that increasing text size is the same idea as adjusting font size, and it will return that article as a relevant result. Similarly, semantic search can use context to resolve ambiguity. For instance, it can determine whether the word “Jaguar” in your query refers to the animal, the car brand, or a sports team based on surrounding context and your intent. By understanding language on a deeper level, semantic search provides results that feel much more intuitive and helpful than a plain keyword lookup.

How Embeddings Power Relevance

An illustration of how queries and documents are represented as points (embeddings) in a vector space. The query (orange) finds a relevant document (blue) by proximity in this space, meaning their vectors are very close.

Under the hood, semantic search relies on embeddings to measure the similarity of meanings. An embedding is essentially a numeric representation of a piece of text – a list of numbers (a vector) that encodes the text’s meaning. Advanced language models (often neural networks) convert words, sentences, or even whole documents into these high-dimensional vectors. The clever part is how these vectors work: texts with similar meanings end up with vectors that are close together, while unrelated texts have vectors far apart when plotted in this multi-dimensional space. For example, the embedding for “car” will be much closer to the embedding for “vehicle” than to that of “banana,” because “car” and “vehicle” have related meanings and “banana” is something completely different. In this way, embeddings capture the semantic relationships between words and phrases.

So, how do embeddings make search results more relevant? When you input a query, the system also turns your query into an embedding vector using the same model. Now both the query and all the documents (or items) in the collection have vector representations. The search system doesn’t just look for matching keywords – instead, it performs a vector similarity search. It compares the query’s vector to the vectors of all the documents to find which ones are closest in value (using a metric like cosine similarity or dot product). If a document’s vector is very close to the query’s vector, it means that document’s content is semantically similar to what you asked. Those documents with the closest vectors are considered the most relevant results. In essence, the engine is saying “find me items that mean the most similar thing to the query,” which is why you can get relevant answers even if they don’t contain the exact query words. This embedding-driven approach is what powers the impressive relevance of semantic search results.

Step-by-Step System Design for Semantic Search

Designing a semantic search system involves a series of steps, from preparing data to handling queries. Here’s a step-by-step breakdown of the system design:

1. Data Preparation: Start by collecting and preprocessing your data. This might be a set of documents, product descriptions, FAQs, or any text you want to make searchable. Clean the text by removing noise (like HTML tags or special characters) and normalize it (for example, lowercasing words). If documents are long, break them into smaller chunks (e.g. paragraphs or sentences) so they fit the input limits of your embedding model. Organizing and enriching your data with metadata (like tags or categories) can also help in later filtering or boosting certain results.

2. Embedding Generation: Next, convert each piece of text into an embedding vector. To do this, choose a suitable embedding model – often a pre-trained language model that can generate embeddings. Popular choices include models like BERT, Sentence-BERT, or OpenAI’s embedding APIs, which are already trained to produce meaningful vectors. You can use these models out-of-the-box, or fine-tune them on your domain data for better accuracy if needed. Each document (or data chunk) is fed into the model to get its vector representation. This step is the core of semantic search, as it translates text information into a mathematical form the system can work with.

3. Vector Indexing: Once you have embeddings for all your items, store them in a database or index that is optimized for similarity search. Traditional databases aren’t efficient for searching by high-dimensional vectors. Instead, semantic search systems use specialized vector databases or indexes. Examples include Facebook’s FAISS, Pinecone, Weaviate, or even Elasticsearch with a plugin for dense vectors. These systems index the vectors using algorithms like HNSW (Hierarchical Navigable Small World) to enable fast approximate nearest neighbor search. In simple terms, they make it possible to search millions of vectors quickly to find those that are closest to a given query vector. Setting up the index might involve choosing parameters that trade off speed vs. accuracy, but for a basic design, the default settings of a good vector index will work well.

4. Query Processing: When a user enters a search query, the system processes it through the same embedding model used in step 2 to produce a query embedding. This ensures the query is represented in the same vector space as the documents. The query’s embedding is then compared against the indexed vectors in the vector database. The index quickly retrieves the most similar vectors – for example, the top N closest matches to the query vector. Those closest matches correspond to the documents or items that are most semantically relevant to the query. This is essentially the “search” step: instead of filtering by keywords, we’re retrieving by vector similarity. The result of this step is a set of candidate results that are likely relevant in meaning.

5. Ranking & Refinement: The initial set of results from the vector search can be further refined to improve accuracy. One common technique is to re-rank the top results using a more precise model or additional criteria. For instance, you might take the top 50 candidates and run a second-pass model (like a cross-encoder that looks at the query and document together) to score them more finely based on relevance. Another best practice is to combine semantic search with traditional keyword search – a hybrid approach. For example, you could first filter documents by a keyword to ensure basic relevancy and then use the embedding similarity to sort those results by meaning. This can give you the precision of keyword matching and the flexibility of semantic understanding. Finally, apply any business-specific ranking tweaks (perhaps boosting newer documents or certain content types) and return the ranked list of results to the user. Throughout this process, it’s important to monitor performance: track metrics like recall (how many relevant items are found in the top results) and latency (search speed), and use caching for frequent queries to make the system more efficient.

By following these steps, you can design a semantic search system that translates users’ natural language queries into meaningful results. Each component – from data prep to embedding, indexing, and querying – plays a role in ensuring the search is both accurate and scalable.

Real-World Use Cases

Semantic search isn’t just a theoretical concept; it’s used widely in many real-world applications. Here are a few areas where semantic search systems shine:

• Search Engines: Modern web search engines (like Google or Bing) heavily utilize semantic search to understand user intent and query context. This allows them to deliver highly relevant results even if you don’t use the exact keywords. For example, Google can interpret a query like “nearby bookshops open now” by understanding location and time context, then show relevant places accordingly. By grasping the meaning behind your words, semantic search makes web searches smarter and more user-friendly.

• E-commerce Product Search: Online retailers use semantic search to help customers find products in a natural way. Shoppers can type something like “comfortable running shoes under $100” and the search system will understand the intent (affordable, running shoes with comfort) and return appropriate items, even if the product descriptions use different wording. This leads to better product discovery – you see relevant options (running shoes within your price range that emphasize comfort) rather than having to perfectly guess the keywords. Semantic search in e-commerce boosts customer satisfaction and sales by connecting people with the products they actually want.

• Customer Support & FAQs: AI-driven chatbots and helpdesk search portals use semantic search to improve support. Often, users ask the same question in many different ways. A traditional keyword search might fail if the phrasing doesn’t match an FAQ article exactly. Semantic search solves this by matching the meaning of the question to the knowledge base. For instance, a user might ask, “Why is my internet slow on my laptop?” and the system can find an article titled “Troubleshooting slow Wi-Fi on notebooks,” recognizing that laptop and notebook are synonyms and the issue is about slow internet. By interpreting queries flexibly, semantic search helps customer support systems deliver the right answers faster, reducing frustration.

• Recommendation Systems: While not a search query from a user per se, recommendation engines benefit from similar embedding technology. Streaming services, shopping platforms, and social media use embeddings to suggest content or products that semantically align with your interests. For example, a music app might recommend songs that “feel” similar to ones you’ve been listening to, using vector embeddings of audio or lyrics. In technical terms, the system finds items with vectors close to those of content you like. Many recommendation systems rely on these vector similarities to make smarter suggestions. This is why you often discover movies or products that match your taste, even if you didn’t explicitly search for them – the system understood the semantic patterns in your preferences.

Best Practices for Building Semantic Search Systems

Building a semantic search system involves more than just plugging in an embedding model. Here are some best practices to ensure your system is effective and robust:

• Choose the Right Embedding Model: Pick an embedding model that fits your domain and use case. General-purpose models like BERT or Sentence Transformers are great starting points. If you have domain-specific data (for example, medical articles or legal documents), consider fine-tuning a model or using a model pre-trained on similar data to capture the nuances better. Also, use the same model for all embeddings (both for queries and documents) to ensure they are comparable – mixing models would result in incompatible vectors.

• Use a Vector Database for Scaling: Storing and searching embeddings efficiently is crucial. Use specialized vector databases or indexes designed for similarity search, such as FAISS, Pinecone, Weaviate, or Milvus. These systems are optimized for handling high-dimensional vectors and can perform fast approximate nearest neighbor lookups on large datasets. A good vector index will allow your semantic search to scale to millions of items while keeping query times low (usually only a few milliseconds). It’s much more efficient than trying to use a traditional database for this task.

• Combine Semantic and Keyword Search: Don’t be afraid to hybridize your search solution. Hybrid search (semantic + keyword) can offer the best of both worlds. For instance, you might use semantic search to capture broad meaning and use keyword filters to ensure certain exact requirements (like a specific tag or category) are present. This way, you guarantee relevance on multiple levels. Combining approaches can especially help when your dataset has critical keywords (product IDs, names, etc.) that should not be ignored, even while you want the flexibility of semantic matching.

• Monitor, Evaluate, and Tune: Treat your semantic search system as an evolving project. Continuously monitor how it’s performing. Track metrics like recall@k (are the correct answers appearing in the top results?) and response time. Evaluate results qualitatively as well – are users clicking the top results or immediately refining their queries? Use this feedback to tune the system. You might adjust the embedding model, tweak the vector index parameters, or add a re-ranking step if needed. Also, implement caching for frequent searches. If many users ask similar questions (e.g. “weather today”), caching those results can significantly speed up response time and reduce load on your system.

• Keep Data and Embeddings Up-to-Date: Semantic search quality is tied to your data freshness. Whenever new content is added or existing content is updated, make sure to generate embeddings for that new/updated data and add them to your index. If your application’s topic or language evolves (say new slang or terms become common), periodically consider updating or retraining your embedding model so it stays current. Regular maintenance like re-indexing and model updates will ensure your search results remain relevant over time.

By following these best practices, you’ll build a semantic search system that not only works well initially but continues to deliver high-quality results as your application grows. Good design and ongoing tuning are key to success in AI system design for search.

Conclusion

Designing a semantic search system using embeddings can greatly enhance the user experience of any search application. By understanding queries on a deeper level, such a system returns results that truly match what the user intends to find. We discussed how semantic search works, the role of embeddings in capturing meaning, and a step-by-step approach to build this system – from data preparation to query processing and refinement. We also explored real-world use cases and best practices, highlighting that semantic search is an important concept in modern system design. For aspiring engineers, mastering semantic search not only helps in building smarter applications but also prepares you for interviews where AI system design concepts are in focus. To continue learning and sharpen your skills (including more on embeddings and other AI fundamentals), check out the Grokking Modern AI Fundamentals course by DesignGurus. It offers a hands-on way to dive deeper into topics like semantic search, with technical interview tips and practical examples to help you become confident in designing intelligent systems. Good luck on your learning journey!

FAQs

Q1: What is semantic search? Semantic search is a search technique that tries to understand the meaning of your query instead of matching just the exact words. It uses AI and language models to interpret what you’re looking for. This means it can find relevant results even if they don’t contain the exact keywords of your search.

Q2: How do embeddings improve search results? Embeddings turn text into numerical vectors that represent meaning. In semantic search, your query and all documents are converted into these vectors. The search system then finds documents with vectors closest to the query’s vector. If the vectors are close, it means the content is similar in meaning, so you get more relevant results based on ideas, not just exact words.

Q3: How is semantic search different from keyword search? Traditional keyword search looks for literal matches of the words you typed. Semantic search, in contrast, looks at the intent and context. It can retrieve answers that use different words but mean the same thing as your query. In short, keyword search is about exact words, while semantic search is about overall meaning. This allows semantic search to handle synonyms or rephrased questions that keyword search might miss.

Q4: Do I need machine learning expertise to implement semantic search? Not necessarily. While semantic search is powered by machine learning models, you don’t have to build those models from scratch. There are many pre-trained embedding models and easy-to-use libraries available. For example, you can call an API to get embeddings for your text, or use open-source tools that handle the heavy lifting. This means even beginners can add semantic search to applications by leveraging existing AI services and tools, without deep ML expertise.