Search engines have undergone a transformation, shifting from simple keyword-based retrieval to systems capable of understanding the context, intent, and semantic relationships of queries. This evolution has been driven by advancements in AI, including NLP and knowledge representation techniques. We will explore key milestones in this journey, from early indexing methods to the integration of semantic search, knowledge graphs, and ML models.

By combining the genAI capabilities of LLM with external knowledge bases, RAG systems offer dynamic, context-aware search experiences. These systems generate precise and personalized answers by leveraging embeddings, neural network architectures, and the ability to integrate relevant external data.

While RAG systems represent a significant leap forward, they also face challenges: reliance on high-quality and unbiased data sources, computational costs associated with processing large datasets, and ensuring transparency in generated outputs.