Is semantic search AI?

Semantic search: the next evolutionary step in AI search

Imagine you could talk to a search engine like you would talk to a friend. Instead of rigid keywords, you use natural language to express your intentions. And the search engine not only understands you, but also delivers results that are exactly what you were looking for – even if you had formulated it differently.

This is the power of semantic search, a technology that powers AI search engines such as Perplexity, Google Gemini and ChatGPT. It is at the center of a quiet paradigm shift in the way we find and consume information online.

In this blog post, we will dive deep into the world of semantic search. We’ll demystify how it works, explore its benefits and challenges, and discuss how it’s changing the landscape of AI search in 2024 and beyond. Whether you’re a tech enthusiast, an entrepreneur, or just curious about the future of search, this guide will give you the knowledge you need to understand – and utilize – semantic search.

The keyword “”semantic search”” is used naturally in this post to maximize both readability and SEO relevance. Expect it to appear several times in different contexts, similar to a conversation about the topic.

What is semantic search?

Forget everything you know about traditional search for a moment. Semantic search is not an improvement on the old way of searching with keywords – it’s a whole new paradigm.

Instead of just looking for matches between the words in your query and the words on a web page, semantic search tries to understand what you really mean. It builds a mathematical representation of your intent, context and the meaning of your words – and then compares it to similar representations from billions of web pages and documents.

How does semantic search work?

Let’s break down the magic behind semantic search in three steps:

1. Create embeddings: Imagine if every word, sentence and concept could be translated into a unique mathematical code – a vector in a high-dimensional space. That’s what AI models like BERT, RoBERTa and GPT do. They take your search query and turn it into a dense series of numbers that encode its meaning.
2. Semantic comparison: Now that your query has been translated into the “”meaning space””, the search engine compares it to the vector representations of billions of web pages and documents. It’s not looking for literal matches, but semantic similarity – content that means what you’re looking for, even if it uses different words.
3. Adjust results: But it doesn’t stop there. Semantic search engines also consider your context – your location, your search history, your device information – to further refine results. Then they apply algorithms that evaluate how helpful, authoritative and relevant each potential result is to your specific intent.

The SEO effect: Why high-quality content is more important than ever

Here’s the kicker for anyone interested in SEO: semantic search rewards content that actually answers questions and solves problems. The days of keyword stuffing are over. AI search engines can now understand the true value of an article – whether it satisfies a query, shows expertise and is trustworthy.

A brief historical context: from keywords to concepts

The journey to semantic search did not start overnight. It is the result of decades of innovation in AI, NLP and search technology. Here’s a quick overview of some key moments:

• 2013: Google introduces the “”Knowledge Graph””, a huge network of entities and their relationships that helps Google to better understand queries.
• 2019: Google’s BERT model revolutionizes natural language processing and enables search engines to understand the context of words in a query
• 2022-2023: Large language models (LLMs) such as GPT-3 and GPT-4 demonstrate unprecedented capabilities in human language understanding and generation, paving the way for AI search engines.

Why semantic search matters (and how it improves AI search)

Imagine you are looking for “the best Italian restaurant for a romantic dinner”. A traditional search engine might only pay attention to the keywords “”Italian restaurant””. But a semantic search engine understands the nuanced intent behind your query – you’re looking for atmosphere, for an experience, not just cuisine.

This is the core of why semantic search matters. It bridges the gap between the way humans think and the way machines process information. And it is changing the landscape of AI search in profound ways.

Here’s how AI search engines benefit from semantic search:

• Improved relevance: By understanding the meaning behind queries, AI search engines can deliver results that are actually relevant to what users are looking for – not just literal matches.
• Higher interaction: When users find what they are looking for (or even what they would have been looking for if they had known it existed), they spend more time on the platform. They ask more questions, explore more content and interact more deeply with the knowledge.
• Conversational AI experiences: Semantic search enables the kind of natural, conversational interactions that power chatbots and AI assistants. It allows you to ask follow-up questions, refine your queries and interact with search in a way that feels intuitive.

Comparison of search types: Boolean, vector and hybrid search

Not all semantic search engines are the same. Different platforms use different approaches, each with their own strengths and weaknesses. Let’s compare three main types:

1. Boolean search: Think of the classic keyword search where you use AND, OR and NOT to narrow down your results. It’s precise if you know exactly what you’re looking for, but unforgiving of errors or variations in the way you phrase your query.
2. Vector search: This is the true semantic search, where queries and documents are encoded into vectors and then compared for similarity. It is excellent at capturing meaning, but sometimes it can return results that are too broad.
3. Hybrid search: The best of all worlds. Hybrid search engines combine Boolean and vector searches and utilize the strengths of both approaches. They can score for both keyword matches and semantic similarity, providing the most balanced and relevant results.

Here is a comparison table to illustrate the differences:

Practical applications of semantic search

Semantic search is not a theoretical technology – it is already changing how we work and live in different industries. Here are some examples:

• E-commerce: Platforms like Shopify use semantic product search to help shoppers find exactly what they are looking for, even if they call it “red sneakers”, “crimson sneakers” or “burgundy sports shoes”.
• Corporate knowledge: Companies such as IBM are integrating semantic search into their internal knowledge databases, enabling employees to find relevant documents, policies and experts with natural language queries.
• Healthcare: Researchers use semantic search to discover relevant studies from millions of scientific articles, speeding up the research process.

The technology behind semantic search (for technology enthusiasts)

If you’re interested in the technical details, let’s take a quick look at the technology that powers semantic search.

Popular AI models for embeddings

Different platforms use different AI models to generate embeddings – some optimized for speed, others for accuracy. Here are some popular options:

• BERT and its variants (RoBERTa, DistilBERT): These models, developed by Google, are particularly good at understanding the context within sentences.
• Sentence Transformers: Based on BERT, these models are optimized for creating sentence or document level embeddings.
• OpenAI models (text-embedding-ada-002): Known for their strong performance in general semantic similarity tasks.

Choosing the right model depends on your specific requirements – do you need the highest accuracy, or is speed and scalability more important?

Indexing and retrieval: How search engines store vectors

Creating embeddings is just the beginning. To efficiently search through billions of vectors, you need specialized infrastructure. This is where vector databases and retrieval engines come into play:

• Vector databases: Systems such as Pinecone, Weaviate and Milvus are designed to store and index high-dimensional vectors.
• Retrieval engines: Platforms such as Elasticsearch and Vespa can handle both traditional reverse indexes and vector indexes and enable hybrid search.

Evaluation of semantic search performance

How do you measure whether your semantic search engine is actually delivering helpful results? Here are some important metrics:

• NDCG (Normalized Discounted Cumulative Gain): Evaluates how well the most relevant results appear at the top of the results page.
• MRR (Mean Reciprocal Rank): Measures how quickly the first relevant hit appears in the results.
• Precision@K: Calculates how many of the top K results are actually relevant.

Challenges and considerations when implementing semantic search

Despite its potential, semantic search faces several challenges:

• Biases in training data: AI models can inherit unintended biases from the data used to train them, which can lead to unfair or inaccurate results.
• Data protection concerns: Collecting and analyzing user interactions to improve search quality raises data protection issues.
• Operationalization: Scaling semantic search in production systems can be complex and resource-intensive.

Conclusion: The future of search is semantic (and AI-driven)

Semantic search is no longer on the horizon – it’s here and it’s already changing how AI search engines interpret and deliver information. As the technology evolves, we can expect even deeper integrations with AI assistants, conversational interfaces and predictive search experiences.

The key insight? Content that provides real value – that answers questions, solves problems and demonstrates expertise – will thrive in the era of semantic search. Whether you’re a developer, marketer or entrepreneur, now is the time to harness the power of semantic search and adapt your content and applications accordingly.

The question is no longer whether semantic search will change the way we search – but how quickly you can adapt to stay ahead.