Matching systems and recommendation engines are something that makes our life easier, because there are a lot of options everywhere, but if you ever searched for a job on the platform like LinkedIn, you may know what flaws recommendation system may have when it comes to a job choice. Typically, we will need to write something like a job title. Then explain the system what are parameters we are interested in. Like location, industry, employment type, or previous experience, and this often results in a clumsy input a typical search engine can't handle accurately. That's where recommendation engines come in handy as they scrap our interest from our activity and try to suggest the most relevant options for us to choose. So when it gets difficult to click on multiple filters and lurk for the correct job title, we try another approach. Natural language processing or NLP is a type of artificial intelligence that works flawlessly with understanding human language and context. In this short demo, we are looking at an NLP model that was trained on a data set of vacancies. So here we can provide our input in a free form so that the model can analyze it. This model uses named entity recognition to understand which words correspond to the important information like job title, location, or industry. Then it searches the database within the website to suggest you most relevant options. User wise, this approach is much more convenient as it allows us to input more precise data without dealing with multiple filters and search for options based on it. But business wise, NLP recommendations can be made for basically any item that has a written description.