One common challenge for users in complex search scenarios is formulating queries that return complete but also relevant results. A frequent issue is term mismatching, where key documents are unintentionally excluded due to differences in terminology, jargon, or phrasing used by authors. This mismatch can not only lead to null search sessions, where queries yield empty result sets, but it also risks overlooking relevant documents. To mitigate this, we propose leveraging Retrieval-Augmented Generation (RAG) to suggest alternative terminology to the user. Unlike traditional query expansion methods that focus on individual terms, our approach produces meaningful keyphrase-level suggestions. Academic and professional search users often use keyphrases to formulate their queries (e.g., “information retrieval” or “natural language generation”), and, by completing these query clauses with synonyms, we aim to retrieve a broader set of relevant documents. In particular, we focus on generating disjunctive clauses for boolean queries, the standard format in complex search engines, allowing the inclusion of concept variations within a single query. Experimental results demonstrate that these keyphrase-based suggestions significantly improve retrieval effectiveness, helping users receive more appropriate results without missing relevant documents due to keyphrase mismatch. (Paper code: github.com/JorgeGabin/RAKS ).