diff --git a/examples/rag/rag_self_correction.py b/examples/rag/rag_self_correction.py
new file mode 100644
index 0000000000..a3455dd128
--- /dev/null
+++ b/examples/rag/rag_self_correction.py
@@ -0,0 +1,152 @@
+from typing import List, Any, Optional, Dict
+
+import logging
+from pprint import pprint
+
+from canals.component.types import Variadic
+from haystack import Pipeline, Document, component, default_to_dict, default_from_dict, DeserializationError
+from haystack.document_stores import InMemoryDocumentStore
+from haystack.components.retrievers import InMemoryBM25Retriever
+from haystack.components.generators import GPTGenerator
+from haystack.components.builders.prompt_builder import PromptBuilder
+from haystack.components.others import Multiplexer
+from haystack.components.routers.conditional_router import ConditionalRouter
+
+
+logging.getLogger().setLevel(logging.DEBUG)
+
+
+@component
+class PaginatedRetriever:
+    """
+    This component is used to paginate the results of a retriever.
+    It is useful when the retriever returns a large number of documents, and we want to pass them to the LLM
+    in batches.
+
+    It is useful in cases where the LLM's context length is limited, and we want to avoid passing too many
+    documents to it at once.
+    """
+
+    def __init__(self, retriever: Any, page_size: int = 1, top_k: int = 100):
+        self.retriever = retriever
+        self.page_size = page_size
+        self.top_k = top_k
+        self.retrieved_documents = None
+
+    def to_dict(self):
+        return default_to_dict(self, retriever=self.retriever.to_dict(), page_size=self.page_size)
+
+    @classmethod
+    def from_dict(cls, data):
+        if not "retriever" in data["init_parameters"]:
+            raise DeserializationError("Missing required field 'retriever' in SlidingWindowRetriever")
+
+        retriever_data = data["init_parameters"]["retriever"]
+        if "type" not in retriever_data:
+            raise DeserializationError("Missing 'type' in retriever's serialization data")
+        if retriever_data["type"] not in component.registry:
+            raise DeserializationError(f"Component type '{retriever_data['type']}' not found")
+        retriever_class = component.registry[retriever_data["type"]]
+
+        data["init_parameters"]["retriever"] = retriever_class.from_dict(retriever_data)
+        return default_from_dict(cls, data)
+
+    @component.output_types(documents=List[Document])
+    def run(
+        self,
+        query: Variadic[str],
+        top_k: Optional[int] = None,
+        filters: Optional[Dict[str, Any]] = None,
+        scale_score: Optional[bool] = None,
+    ):
+        if not top_k:
+            top_k = self.top_k
+
+        if self.retrieved_documents is None:
+            self.retrieved_documents = self.retriever.run(
+                query=query[0], filters=filters, top_k=top_k, scale_score=scale_score
+            )["documents"]
+
+        if not self.retrieved_documents:
+            raise ValueError("No more documents available :(")
+
+        next_page = self.retrieved_documents[: self.page_size]
+        self.retrieved_documents = self.retrieved_documents[self.page_size :]
+        return {"documents": next_page}
+
+
+def self_correcting_pipeline():
+    # Create the RAG pipeline
+    rag_pipeline = Pipeline(max_loops_allowed=10)
+    rag_pipeline.add_component(instance=Multiplexer(str), name="query_multiplexer")
+    rag_pipeline.add_component(
+        instance=PaginatedRetriever(InMemoryBM25Retriever(document_store=InMemoryDocumentStore())), name="retriever"
+    )
+    rag_pipeline.add_component(
+        instance=PromptBuilder(
+            template="""
+    Given these documents, answer the question.
+    If the documents don't provide enough information to answer the question, answer with the string "UNKNOWN".
+
+    Documents:
+    {% for doc in documents %}
+        {{ doc.content }}
+    {% endfor %}
+
+    Question: {{question}}
+    Answer:
+    """
+        ),
+        name="prompt_builder",
+    )
+    rag_pipeline.add_component(instance=GPTGenerator(), name="llm")
+    rag_pipeline.add_component(
+        instance=ConditionalRouter(
+            routes=[
+                {
+                    "condition": "{{ 'UNKNOWN' in replies|join(' ') }}",
+                    "output": "{{ query }}",
+                    "output_name": "unanswered_query",
+                    "output_type": str,
+                },
+                {
+                    "condition": "{{ 'UNKNOWN' not in replies|join(' ') }}",
+                    "output": "{{ replies }}",
+                    "output_name": "replies",
+                    "output_type": List[str],
+                },
+            ]
+        ),
+        name="answer_checker",
+    )
+
+    rag_pipeline.connect("query_multiplexer", "retriever")
+    rag_pipeline.connect("query_multiplexer", "prompt_builder.question")
+    rag_pipeline.connect("query_multiplexer", "answer_checker.query")
+    rag_pipeline.connect("retriever", "prompt_builder.documents")
+    rag_pipeline.connect("prompt_builder", "llm")
+    rag_pipeline.connect("llm.replies", "answer_checker.replies")
+    rag_pipeline.connect("answer_checker.unanswered_query", "query_multiplexer")
+
+    # Draw the pipeline
+    rag_pipeline.draw("self_correcting_pipeline.png")
+
+    # Populate the document store
+    documents = [
+        Document(content="My name is Jean and I live in Paris."),
+        Document(content="My name is Mark and I live in Berlin."),
+        Document(content="My name is Giorgio and I live in Rome."),
+        Document(content="My name is Juan and I live in Madrid."),
+    ]
+    rag_pipeline.get_component("retriever").retriever.document_store.write_documents(documents)
+
+    # Query and assert
+    question = "Who lives in Germany?"
+
+    result = rag_pipeline.run({"query_multiplexer": {"value": question}})
+
+    pprint(result)
+
+
+if __name__ == "__main__":
+    self_correcting_pipeline()
diff --git a/releasenotes/notes/self-correcting-rag-2e77ac94b89dfe5b.yaml b/releasenotes/notes/self-correcting-rag-2e77ac94b89dfe5b.yaml
new file mode 100644
index 0000000000..f0d3c1f92b
--- /dev/null
+++ b/releasenotes/notes/self-correcting-rag-2e77ac94b89dfe5b.yaml
@@ -0,0 +1,2 @@
+enhancements:
+  - Add RAG self correction loop example