SEO so many times, its name has changed a few times, and yet it's still here. In practice, process optimization wasn't invented by the pioneers who first saw that search engine called Google working.

In our new world, so dynamic and intricate, this vast contemporary organic and digital , search engine optimization has transcended its essence. From the mere manipulation of keywords , focused on manipulating the search system and conquering the top positions in the results, to today, gripped by the fear of not knowing what to do to be cited by ChatGPT, our function continues to be understanding how a search engine works. Then the work is to format the information we have so that it can be retrieved by these tools (or become a source for generating a response).

Purist of the term, any system that offers a search functionality—Google, an e-commerce site's internal search engine, or even virtual assistants—is, by definition, a search tool. Therefore, the act of optimizing, or creating content , which manifests as information, data , or any other representation , so that these systems deliver answers effectively, whether retrieving or generating information, remains SEO at its core.

Those who follow me on LinkedIn know my opinion on this emergence of new acronyms to describe this activity. But this text isn't about that. It's something much more technical, but it reflects my understanding of the new search landscape, and I'm sharing my thoughts on it.

Can vector data help redefine semantic SEO?

I wrote this article because I decided to understand this transformation that is reshaping how we interact with information online. For us, SEO professionals, and for any individual immersed in the digital flow, understanding these concepts is a way to decipher the complexities of the present and the directions of the future.

But what is vector data?

Vector data are numerical representations of objects, concepts, or words, expressed as points in a multidimensional space. In the study of semantic search and Artificial Intelligence (AI) applications, these vectors capture the essential characteristics and semantic relationships of the original data, which can be words, images, videos, or audio. Each dimension of the vector corresponds to a specific characteristic, and the proximity between two vectors in this space indicates the semantic similarity between the items they represent. This ability to quantify similarity makes vector data fundamental to AI, especially in tasks that require understanding context and meaning.

Applications in semantic search

In semantic search, vector data, often generated by embedding , allows systems to understand the meaning behind queries , rather than simply matching exact keywords. For example, a query like "science fiction films about space" can be converted into a vector that is semantically close to vectors representing films like "2001: A Space Odyssey" or "Interstellar," even if the exact words are not present in the titles. This significantly improves the relevance of search results, providing a more intuitive and effective user experience

Applications in Artificial Intelligence

In Artificial Intelligence applications, vector data is the backbone of many machine learning algorithms. It is used in Natural Language Processing (NLP) to represent words and phrases, allowing models to understand and generate text. In computer vision, images and their features are transformed into vectors for tasks such as facial recognition and classification . Furthermore, recommendation systems utilize vector data to identify patterns and suggest relevant items, while anomaly detection can find unusual patterns in large vector-represented datasets. The ability to represent complex information in a standardized and computable way is what makes vector data an indispensable tool in today's AI landscape.

The semantic gap and the challenge of traditional search.

Historically, the way computers store and process data is very different from how humans assign meaning to information. Conventional databases operate with structured fields, where information is categorized by file format, creation date, or manually entered tags. While this is effective for structuring data, this approach creates some serious problems when it comes to capturing the overall context of unstructured data, such as images, text, and audio. And imagine the sheer amount of unstructured data versus structured data posted on the internet every day?

This disconnect creates what the technology world calls a "semantic gap": a barrier between the computational storage of data and human understanding of its inherent meaning.

Imagine having a vast library organized by book size, but without a system that connects the books by their themes or content, making the search for something relevant an arduous and often unproductive, if not impossible, task.

How do we fill this gap and reveal the meaning?

Is this where vector databases emerge as a lifesaver? No, but they can certainly help us solve many different problems.

Unlike their predecessors, relational databases, they were created to store and retrieve data in the form of "vector embeddings," that is, arrays of numbers. The main aspect of these vectors is that they encode the "semantic essence" of the information. In other words, items with similar meanings are positioned close together in the vector space, while dissimilar items are spaced further apart.

Visualization of the Semantic Vector Space

As discussed, vector databases store information as “vector embeddings”—arrays of numbers that encode the “semantic essence” of the data. In this simplified visualization of a 2D space, you can see how items with similar meanings (e.g., “Apple (fruit)” and “Orange”) are positioned close to each other, forming semantic clusters. In contrast, items with distinct meanings (such as “Apple (fruit)” and “Apple (company)”) are further apart, demonstrating the proximity and distance relationship based on meaning.

This ability to represent meaning allows for similarity searches that go beyond the exact matching of terms. Anyone who uses a modern search engine knows that it's possible to search for content that is semantically similar, even if the words are not identical.

Imagine searching for “images with color palettes similar to a sunset in the mountains” or “landscapes with similar attributes”; if your search is semantic or hybrid, the chance of a good result is much higher. And vector databases make this a tangible reality, through mathematical operations that identify vectors close to each other in multidimensional space.

Vector embeddings, their models and dimensions

The creation of these vectors that capture meaning is a complex process, difficult to understand, but fascinating. Vector embeddings are generated by "embedding models" that are trained on vast datasets. At the beginning of the Artificial Intelligence boom, these models were trained with trillions of texts, a text corpus. Nowadays, with multimodal search, we have text, image, video, and audio as part of this gigantic "database," hence the name Large Language Model.

The diversity of data requires specialized models: for example, the CLIP model is important for images, Glove for text, and Wave to Vec for audio.

When unstructured data, whether an image, a text excerpt, or an audio file, is processed by an embedding model, it goes through multiple layers of processing. Each layer has the ability to extract increasingly abstract features:

• For an image, the initial layers can identify edges and textures, while the deeper layers recognize complete objects and scenes.
• In the case of text, the first layers process individual words, and the subsequent layers understand the context and overall meaning.

The end result is a high-dimensional vector (potentially having hundreds or thousands of dimensions) that encapsulates the essential characteristics of the input, representing its meaning in a mathematically comparable way.

Efficiency in search: vector indexing and ANN algorithms.

With millions, or even billions, of high-dimensional vectors in a database, comparing a query vector with every existing vector would be a prohibitively slow and progressively expensive task. To overcome this limitation, "vector indexing" employs Approximate Nearest Neighbor (ANN) .

ANN algorithms sacrifice a small amount of precision in favor of substantially faster search speeds. Instead of finding the closest exact match, they quickly locate vectors that are very likely to be among the closest, making similarity searching on large volumes of data extremely efficient.

It is because of this factor that we can state, with a certain degree of adaptation, that a generative model is always working at the average.

Notable examples of indexing methods include HNSW (Hierarchical Navigable Small World), which constructs graphs , and IVF (Inverted File Index), which segments the vector space into clusters. This efficiency is one of the foundations for the performance of AI applications and large-scale semantic search, which is why we have very fast answers to increasingly complex questions.

RAC and the evolution of semantic search

Practical applications of vector databases are becoming increasingly common, especially in the field of applied Artificial Intelligence. A growing example is RAG , or Augmented Generation Retrieval .

RAG is an Artificial Intelligence architecture that combines the ability of Large Language Models (LLMs) to generate text with the ability of information retrieval systems to seek relevant data from external sources. Essentially, RAG allows LLMs to go beyond their pre-trained knowledge by accessing and incorporating up-to-date, domain-specific information before formulating a response. This helps limit problems inherent in traditional LLMs: the tendency to "hallucinate" (generate incorrect or fabricated information) and the reliance on static and potentially outdated data.

How does RAG work?

The RAG process generally involves two main phases:

1. Retrieval: When a query is made to the RAG system, a retrieval component is activated. This component searches an external knowledge base (which may be a database, internal company documents, the internet, etc.) for information that is semantically relevant to the user's query. This search generally uses semantic search techniques, where the query and documents are represented as vectors (vector data) in a multidimensional space, and vector proximity indicates relevance.
2. Generation: The most relevant information retrieved in the previous phase is then provided to the LLM along with the user's original query. The LLM uses this additional context, i.e., the "augmented" information, to generate a more accurate, factual, and contextualized response. This ensures that the model's output is based on verifiable data, rather than relying solely on the model's internal memory acquired during training.

So, as you can see, in this context, vector databases store fragments of documents, articles, and knowledge bases as embeddings and help LLMs generate better answers.

When a user asks a question, the system employs vector similarity search to identify the most relevant text fragments that semantically resemble the query. These fragments are then provided to the model, which uses them to formulate accurate and contextually informed answers.

For you, my friend, who reads the Semantic Blog, and for everyone seeking to deepen their understanding of the new landscape of semantic and AI-driven searches, understanding these concepts is the gateway to a new world of SEO.

Semantic search, which goes beyond simple keyword matching, and the rise of Artificial Intelligence as an engine for information retrieval and generation, are these the forces shaping the future of human interaction with knowledge? We'll see what the future holds, but we're leaning towards that.

The main objective of search, since its origins, has always been to connect a person's informational need with the information that satisfies it. This basic premise remains unchanged. What evolves, incredibly, is the "in-between": the technologies and methodologies that make this connection increasingly fluid, intuitive, and precise.

Vector databases represent a significant advancement in this regard , and we in SEO use the tools where they are present, without knowing what's behind them. But this database model allows machines not only to find data and transform it into information, but also greatly improves the understanding and manipulation of the intrinsic meaning within it.

My vision, grounded in nearly two decades of work in this field, is that SEO, at its core, is not just about optimizing for search engines, but about optimizing for the human search experience. I believe that ensuring that information, in its full meaning, is discovered, understood, and used by the people who need it is my personal and professional goal.

In this scenario, vector databases and semantic search are more than just technological trends; they are the backbone of a future where information is revealed in its richest and most accessible form.

But how can this semantic search scenario be applied in practice, or at least in an exercise of imagining what an application would look like? I tried to imagine four scenarios:

• discovery of knowledge (I pulled the coals to academic research);
• dissemination of news and information;
• combating misinformation;
• The future of search technology..

Therefore, I did more research, enlisted the help of my research partner, NotebookLM, and tried to answer these questions. Let's get to them:

What is the impact of the search revolution on academic research and knowledge discovery?

The transition from keyword-based search to semantic search, which uses vector data to understand meaning and context, has the potential to transform how researchers discover knowledge. This is already happening today, but we don't yet have research demonstrating this shift.

Instead of relying on exact combinations of technical terms, an academic could conduct conceptual searches, finding relevant studies and articles that address the same idea, even if with different terminology. I just told you that I use Google's NotebookLM to set up a study and research environment, and I also use Gemini in its Deep Research function as my personal reference librarian to develop search strategies for myself.

The fact that I am a library science student helps me to assess the quality of the research he puts together and to be able to customize it, avoiding mistakes.

Would this evolution make the literature review process more efficient and comprehensive? Perhaps by working in conjunction with Retrieval Augmented Generation (RAG), which I mentioned earlier. This combination would allow AI systems to query large academic databases chosen by users to generate abstracts, identify connections between different fields of study, and answer complex research questions.

This would accelerate knowledge discovery by connecting information in a more precise and intuitive way, aligning the researcher's informational needs with the most relevant data available.

How has the search revolution affected the dissemination of news and information?

We know that technology fundamentally alters the dissemination of any type of information. One of the changes lies in how information needs to be structured to be found. This new era demands that content, including news, be formatted in a way that new search tools, such as those using generative AI, can not only index but understand and use to build answers. This creates a need for technical knowledge that most professionals lack.

This means that the visibility of a news story will no longer depend solely on traditional SEO techniques. The emphasis may be shifting towards greater clarity, more semantic depth, and the reliability of the content. Will news portals that manage to present their information in a structured and context-rich way be more likely to be used as a source by generative AIs? Only the future will tell.

Dissemination is becoming less about "appearing first" and more about "being the source of the answer," changing the paradigm of how information reaches the public. And we face many challenges in this major shift, because now it's the Big Tech companies, the owners of the tools, who are calling the shots.

How is the search revolution impacting the fight against misinformation?

The articles I read as the basis for this piece suggest a promising path for combating misinformation, centered on the AI ​​architecture known as Retrieval Augmented Generation (RAG).

The major problem with generative AI is hallucination, that is, the creation of false but plausible information. Misinformation thrives in this environment. The RAG model directly impacts this problem by forcing the AI ​​to base its responses on information retrieved from an external and verifiable database, instead of freely generating text.

This mechanism functions as a fact filter. Before formulating an answer, the system first searches for and retrieves relevant data from reliable sources. Only based on this retrieved data does the AI ​​generate the final answer. By basing content generation on sources that can be audited and considered trustworthy, the potential for AI to fabricate information is drastically limited. Therefore, the search revolution, by adopting this architecture, increases the accuracy and factual nature of the answers, becoming a powerful tool to mitigate the spread of misinformation.

The use of Knowledge Graphs in conjunction with ontologies can be a path to building RAC tools.

What are the predictions for the future of search technology in the next 5 years?

The future of search in the next five years will be increasingly semantic and driven by artificial intelligence; that much is clear to me.

Exact keyword matching is already obsolete and will have to be replaced by systems that understand the need, intent, and context behind the user's query. This will be possible thanks to the widespread adoption of technologies such as vector databases, which I discussed in this article, that translate any type of content into numerical representations of its meaning, allowing for a much more precise connection between the question and the answer.

Furthermore, information retrieval and generation tools, or agents, trained in very specific subjects, guided by knowledge graphs and specialized ontologies, should be the way to create highly impactful tools in how we search for information on a daily basis.

As a result, human interaction with knowledge will become more fluid and intuitive.

The barriers between a person's question and the information they need will be reduced, with technology acting as an almost invisible bridge. In this scenario, SEO work will also evolve. Optimization will no longer be about manipulating algorithms, but about structuring information in the clearest and most understandable way possible so that AIs can discover, validate, and use it. The essence of SEO, connecting people to information, will remain, but tactics will focus on optimizing the human search experience in an AI-driven world.

Thank you for reading this far in such dense content. I hope it has helped you better understand this topic. I'm available on LinkedIn to delve deeper into these subjects. Let's continue talking, studying, practicing, and optimizing!