diff --git a/examples/seq2seq_replacement.py b/examples/seq2seq_replacement.py
new file mode 100644
index 000000000..534a9592d
--- /dev/null
+++ b/examples/seq2seq_replacement.py
@@ -0,0 +1,28 @@
+from haystack import Document
+from haystack.nodes import PromptNode, PromptTemplate
+
+p = PromptNode("vblagoje/bart_lfqa")
+
+# Start by defining a question/query
+query = "Why does water heated to room temperature feel colder than the air around it?"
+
+# Given the question above, suppose the documents below were found in some document store
+documents = [
+    "when the skin is completely wet. The body continuously loses water by...",
+    "at greater pressures. There is an ambiguity, however, as to the meaning of the terms 'heating' and 'cooling'...",
+    "are not in a relation of thermal equilibrium, heat will flow from the hotter to the colder, by whatever pathway...",
+    "air condition and moving along a line of constant enthalpy toward a state of higher humidity. A simple example ...",
+    "Thermal contact conductance. In physics, thermal contact conductance is the study of heat conduction between solid ...",
+]
+
+
+# Manually concatenate the question and support documents into BART input
+# conditioned_doc = "<P> " + " <P> ".join([d for d in documents])
+# query_and_docs = "question: {} context: {}".format(query, conditioned_doc)
+
+# Or use the PromptTemplate as shown here
+pt = PromptTemplate("lfqa", "question: {query} context: {join(documents, delimiter='<P>')}")
+
+res = p.prompt(prompt_template=pt, query=query, documents=[Document(d) for d in documents])
+
+print(res)
diff --git a/haystack/nodes/answer_generator/transformers.py b/haystack/nodes/answer_generator/transformers.py
index a4b947096..07ce857e9 100644
--- a/haystack/nodes/answer_generator/transformers.py
+++ b/haystack/nodes/answer_generator/transformers.py
@@ -1,3 +1,4 @@
+import warnings
 from typing import Dict, List, Optional, Union
 
 import logging
@@ -82,6 +83,8 @@ class RAGenerator(BaseGenerator):
         devices: Optional[List[Union[str, torch.device]]] = None,
     ):
         """
+        This component is now deprecated and will be removed in future versions. Use `PromptNode` instead of `RAGenerator`.
+
         Load a RAG model from Transformers along with passage_embedding_model.
         See https://huggingface.co/transformers/model_doc/rag.html for more details
 
@@ -110,6 +113,12 @@ class RAGenerator(BaseGenerator):
                         [torch.device('cuda:0'), "mps", "cuda:1"]). When specifying `use_gpu=False` the devices
                         parameter is not used and a single cpu device is used for inference.
         """
+        warnings.warn(
+            "`RAGenerator` component is deprecated and will be removed in future versions. Use `PromptNode` "
+            "instead of `RAGenerator`.",
+            category=DeprecationWarning,
+        )
+
         super().__init__(progress_bar=progress_bar)
 
         self.model_name_or_path = model_name_or_path
@@ -345,6 +354,8 @@ class Seq2SeqGenerator(BaseGenerator):
         devices: Optional[List[Union[str, torch.device]]] = None,
     ):
         """
+        This component is now deprecated and will be removed in future versions. Use `PromptNode` instead of `Seq2SeqGenerator`.
+
         :param model_name_or_path: A Hugging Face model name for auto-regressive language model like GPT2, XLNet, XLM, Bart, T5, and so on.
         :param input_converter: An optional callable to prepare model input for the underlying language model
                                 specified in the `model_name_or_path` parameter. The required `__call__` method signature for
@@ -367,6 +378,11 @@ class Seq2SeqGenerator(BaseGenerator):
                         [torch.device('cuda:0'), "mps", "cuda:1"]). When specifying `use_gpu=False` the devices
                         parameter is not used and a single cpu device is used for inference.
         """
+        warnings.warn(
+            "`Seq2SeqGenerator` component is deprecated and will be removed in future versions. Use `PromptNode` "
+            "instead of `Seq2SeqGenerator`.",
+            category=DeprecationWarning,
+        )
         super().__init__(progress_bar=progress_bar)
         self.model_name_or_path = model_name_or_path
         self.max_length = max_length