diff --git a/python/pathway/xpacks/llm/question_answering.py b/python/pathway/xpacks/llm/question_answering.py
index 6bb607a0f..12fe609b7 100644
--- a/python/pathway/xpacks/llm/question_answering.py
+++ b/python/pathway/xpacks/llm/question_answering.py
@@ -462,6 +462,11 @@ class BaseRAGQuestionAnswerer(SummaryQuestionAnswerer):
         search_topk: Top k parameter for the retrieval. Adjusts number of chunks in the context.
         rerank_topk: Number of top-scoring documents to retain after reranking, when a reranker is provided.
             If ``None``, reranking is disabled. Defaults to ``None``.
+        query_transformer: Transformer for query rewriting before retrieval.
+            Can be a ``pw.UDF``, callable, or ``None`` to skip query transformation.
+            Available options: ``pathway.xpacks.llm.prompts.prompt_query_rewrite``,
+            ``pathway.xpacks.llm.prompts.prompt_query_rewrite_hyde``.
+            Defaults to ``None``.
 
 
     Example:
@@ -523,11 +528,13 @@ def __init__(
         search_topk: int = 6,
         reranker: pw.UDF | None = None,
         rerank_topk: int | None = None,
+        query_transformer: pw.UDF | Callable | None = None,
     ) -> None:
 
         self.llm = llm
         self.indexer = indexer
         self.reranker = reranker
+        self.query_transformer = query_transformer
 
         if default_llm_name is None:
             default_llm_name = llm.model
@@ -637,7 +644,10 @@ def add_score_to_doc(doc: pw.Json, score: float) -> dict:
     @pw.table_transformer
     def answer_query(self, pw_ai_queries: pw.Table) -> pw.Table:
         """Answer a question based on the available information."""
-
+        if self.query_transformer is not None:
+            pw_ai_queries += pw_ai_queries.select(
+                prompt=self.query_transformer(pw.this.prompt)
+            )
         pw_ai_results = pw_ai_queries + self.indexer.retrieve_query(
             pw_ai_queries.select(
                 metadata_filter=pw.this.filters,