In today's world, where we're flooded with information, having a smart and efficient search system is more important than ever. Traditional search engines like Google rely on keywords and fixed ranking systems such as PageRank. While these methods work well, they often fail to truly understand what a user means, handle complex multi-step questions, or deliver deeply personalized results beyond just rewording queries. Recent advancements in AI, especially large language models (LLMs), have given rise to tools like Perplexity.ai and You.com, which combine search results into easy-to-read summaries. However, these tools still have limitations they lack deep personalization, emotional understanding, field-specific tuning, and adaptability to a user's evolving search journey. This study presents a next-generation AI-powered search engine that bridges these gaps. It combines Google's Custom Search API for scalability with advanced natural language processing for contextual understanding and intelligent recommendation systems. What sets this system apart is its ability to build a growing map of a user's knowledge over time. It dynamically adapts to multi-step queries and continuously refines results to match the user's needs and learning path. Our approach aims to connect the precision of keyword-based searches with the flexibility of conversational, chat-style searches. The result is more relevant answers, reduced search fatigue, and a smoother, more personalized experience especially valuable for academic research, technical exploration, and other knowledge-intensive tasks.
Pawar et al. (2026) studied this question.