The internet as we know it is driven by the Search Engine Results Page (SERP). The SERP is not only an integral part of our online browsing experience but and represents how most of us have come to see the internet. Whether you use Google or any other search engine, you most certainly use SERPs to access and browse the internet. However, as search engines have evolved, there is a growing need for more personalized, efficient, and accurate search results.

In the early days of the internet, people would enter specific keywords or phrases to find relevant web pages. While this approach is still widely used, it has its limitations. As a result, natural language search has become increasingly important, and there is a growing consensus that natural language search will be the future of search engines. While the importance of keywords is still relevant, natural language search is more efficient, useful, and personalized.

What is Natural Language Search?

Natural Language Search (NLS) allows users to search for information in a way that mimics how we speak. This type of search is already integrated into many applications, including mobile phones and voice recognition systems like Siri, Bing, and Google Now. It provides more precise, contextual prompts or requests, and can deliver a more personalized experience.  

NLS makes use of a machine learning technique called Natural Language Processing (NLP) to enable searching for data online using human language instead of keywords. With NLS, users can express their search queries in a more natural way, which is then translated into a form that the computer can understand.

Why Do We Need Natural Language Search?

Traditional keyword-based search engines, such as Google, present information on a wide range of topics by analyzing keywords entered in a query to retrieve relevant information. Picking out the most relevant and accurate information is what Google refers to as ‘optimized content,’ and the entire field of Search Engine Optimization (SEO) has developed around making your content relevant enough for Goggle to rank it above any other. The results are then presented in the form of a SERP.

However, this method has its limitations and cannot guarantee the accuracy of the information presented. NLS offers an alternative approach that is particularly useful in situations where a keyword-based search may not provide the desired results, such as when searching for information that does not fit into a specific category or involves precise, contextual information.  

How Will Search Engines Evolve?

The future of search technology is heavily dependent on the ability to understand and interpret natural language queries. By being able to parse the user's intended meaning, search engines can provide more relevant and accurate results.

This advancement in NLP technology has made search engines more intuitive and user-friendly, allowing people to easily find what they're looking for even if they have limited knowledge about a particular topic. As NLP technology continues to evolve, we can expect search engines to become even more effective at understanding and fulfilling natural language queries.

How NLS Can Improve Search Engines

Another advantage of NLS is the ability to handle complex queries.  

With keyword-based search, it can be difficult for users to find what they're looking for when their query contains multiple concepts or is phrased in a specific way. With NLS, however, the search engine can parse out the different components of a query and provide results that are tailored to the user's needs.

In addition to improving the user experience, NLS can also help search engines better understand user behavior and preferences. By analyzing the queries entered by users, NLS can provide valuable insights into what people are searching for and what they are interested in. This information can then be used to improve the search results and make the overall search experience even better.

Overall, NLS has the potential to revolutionize the way people search for information online. By allowing search engines to understand the meaning behind queries, NLS can help improve the accuracy and relevance of search results, making it easier for users to find what they are looking for.

Search Engines of the Future

The future of search engines is an exciting topic as we can expect to see significant advancements in their capabilities. However, it is important to note that the exact features and capabilities of future search engines are still unknown and subject to change. The potential areas of improvement such as understanding user intent, providing personalized results, and improving mobile experiences, are all possible areas where we may see advancements in the future. However, it is still uncertain to what extent these features will be integrated into future search engines.

The use of knowledge graphs and other methods to understand relationships between people, places, and things is a current trend in search engine technology, and it is possible that this trend will continue in the future. The issue of online privacy and security is becoming increasingly important for users, and search engines may need to address these concerns in the future.  

Additionally, search engines may become better at providing relevant results based on the user's location, but the extent to which this feature will be incorporated into future search engines is yet to be seen.

NLS Powered Chatbots vs SERP

Overall, the choice between keyword-based search and NLS depends on the individual user's needs and preferences. Nevertheless, the fact of the matter is that NLP powered search can be more useful, since bots like ChatGPT have access to the entire body of human knowledge on the internet to draw from. But what separates ChatGPT from Google is the ability to sift through this knowledge to find the most relevant material. This makes NLS powered AI Chatbots very powerful tools that may be capable of supplanting SERP as the standard way of internet search.  

One of the limiting factors of NLS is one that it shares with the SERP model, i.e., result accuracy. However, as NLS evolves and as AI Chatbots become increasingly more sophisticated, their ability to detect inaccurate information will substantially increase, further diminishing the relevance of the SERP. An example of this is asking ChatGPT to solve a coding problem. ChatGPT will present a clearly formatted snippet of code that will most probably be correct, so long as the query is within its ability to solve. Google, on the other hand, will return a host of links based on what it thinks are the best results and let you find out for yourself. The problem with Google’s model is obvious: it does not represent the best or the most accurate data, merely the most optimized data. While Google goes to great lengths to ensure that the information it displays on the top of its SERP is the most relevant, this is not always the case. And unless Google resolves this, NLS chatbots will continue to encroach on its turf.

But therein lies the problem. How does Google resolve the inherent limitations of the SERP mode of internet search without killing it, and in doing so, supplanting its greatest source of income? The answer, for now, seems to be incorporating NLS into its search engine. Whether this will work in the long run remains to be seen.

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