Recommendation systems date back to the early 90's where they were first developed by researchers to suggest important documents to the readers based on the review ratings provided by other users.It became mainstream after tech giants like Google, Amazon, Netflix, Spotify and others integrated it with their systems to recommend relevant content and products to the users. Especially as personalized recommendations gained traction so did recommendation system algorithms. These systems are behind suggesting movies, ads, youtube videos, music to the users based on their interests or we can say based on their predicted interests.

Using maths as a tool, researchers have been able to predict user's interest based on explicit reviews or by collecting information from similar users or using metadata like watch time, clicks etc (implicit review). In this case study, we analyze two of the most popular approaches used in recommendation systems.

RAG (Retrieval-Augmented Generation) systems combine information retrieval and text generation to enhance the performance of language models. They leverage large datasets to retrieve relevant information and provide more accurate and contextually appropriate responses.

They enhance the accuracy and relevance of AI-generated content by combining information retrieval with text generation. This leads to more precise and contextually appropriate responses. Enterprises can benefit from improved decision-making and problem-solving, as RAG systems can quickly access and synthesize vast amounts of data. Additionally, these systems support better customer service by providing detailed and accurate information. They also streamline content creation, reducing the time and effort required.