Relevancy is the root of a search engine's value proposition and can be defined as the art of ranking content for a user's search based on how much that content satisfies the needs of the user or the business.

In an application, it does not matter how beautiful your user interface looks or how many functionalities you are providing to the user; search relevancy cannot be avoided at any cost. So, despite of the mystical behavior of search engines, you have to find a solution to get relevant results. The relevancy becomes more important because a user does not care about the whole bunch of documents that you have. The user enters their keywords, selects filters, and focuses on a very small amount of data—the relevant results. And if your search engine fails to deliver according to expectations, the user might be annoyed, which might be a loss for your business.

A search engine like Elasticsearch comes with a built-in intelligence. You enter the keyword and within the blink of an eye, it returns to you the results that it thinks are relevant according to its intelligence. However, Elasticsearch does not have a built-in intelligence according to your application domain. The relevancy is not defined by a search engine; rather it is defined by your users, their business needs, and the domains. Take an example of Google or Twitter; they have put in years of engineering experience, but still fail occasionally while providing relevancy. Don't they?

Further, the challenges of searching differ with the domain: the search on an e-commerce platform is about driving sales and bringing positive customer outcomes, whereas in fields such as medicine, it is about the matter of life and death. The lives of search engineers become more complicated because they do not have domain-specific knowledge, which can be used to understand the semantics of user queries.

However, despite of all the challenges, the implementation of search relevancy is up to you, and it depends on what information you can extract from the users, their queries, and the content they see. We continuously take feedback from users, create funnels, or enable loggings to capture the search behavior of users so that we can improve our algorithms to provide relevant results.