  Conversational search systems enable information retrieval via natural
language interactions, with the goal of maximizing users' information gain over
multiple dialogue turns. The increasing prevalence of conversational interfaces
adopting this search paradigm challenges traditional information retrieval
approaches, stressing the importance of better understanding the engineering
process of developing these systems. We undertook a systematic literature
review to investigate the links between theoretical studies and technical
implementations of conversational search systems. Our review identifies
real-world application scenarios, system architectures, and functional
components. We consolidate our results by presenting a layered architecture
framework and explaining the core functions of conversational search systems.
Furthermore, we reflect on our findings in light of the rapid progress in large
language models, discussing their capabilities, limitations, and directions for
future research.
