  CIS is a prominent area in IR which focuses on developing interactive
knowledge assistants. These systems must adeptly comprehend the user's
information requirements within the conversational context and retrieve the
relevant information. To this aim, the existing approaches model the user's
information needs by generating a single query rewrite or a single
representation of the query in the query space embedding. However, to answer
complex questions, a single query rewrite or representation is often
ineffective. To address this, a system needs to do reasoning over multiple
passages. In this work, we propose using a generate-then-retrieve approach to
improve the passage retrieval performance for complex user queries. In this
approach, we utilize large language models (LLMs) to (i) generate an initial
answer to the user's information need by doing reasoning over the context of
the conversation, and (ii) ground this answer to the collection. Based on the
experiments, our proposed approach significantly improves the retrieval
performance on TREC iKAT 23, TREC CAsT 20 and 22 datasets, under various
setups. Also, we show that grounding the LLM's answer requires more than one
searchable query, where an average of 3 queries outperforms human rewrites.
