  Information retrieval is a rapidly evolving field of information retrieval,
which is characterized by a continuous refinement of techniques and
technologies, from basic hyperlink-based navigation to sophisticated
algorithm-driven search engines. This paper aims to provide a comprehensive
overview of the evolution of Information Retrieval Technology, with a
particular focus on the role of Large Language Models (LLMs) in bridging the
gap between traditional search methods and the emerging paradigm of answer
retrieval. The integration of LLMs in the realms of response retrieval and
indexing signifies a paradigm shift in how users interact with information
systems. This paradigm shift is driven by the integration of large language
models (LLMs) like GPT-4, which are capable of understanding and generating
human-like text, thus enabling them to provide more direct and contextually
relevant answers to user queries. Through this exploration, we seek to
illuminate the technological milestones that have shaped this journey and the
potential future directions in this rapidly changing field.
