diff --git a/tests/entrypoints/llm/test_prompt_validation.py b/tests/entrypoints/llm/test_prompt_validation.py
new file mode 100644
index 0000000000000..565dfa01346cc
--- /dev/null
+++ b/tests/entrypoints/llm/test_prompt_validation.py
@@ -0,0 +1,9 @@
+import pytest
+
+from vllm import LLM
+
+
+def test_empty_prompt():
+    llm = LLM(model="gpt2")
+    with pytest.raises(ValueError, match='Prompt cannot be empty'):
+        llm.generate([""])
diff --git a/tests/entrypoints/openai/test_prompt_validation.py b/tests/entrypoints/openai/test_prompt_validation.py
new file mode 100644
index 0000000000000..0a573a0066d32
--- /dev/null
+++ b/tests/entrypoints/openai/test_prompt_validation.py
@@ -0,0 +1,22 @@
+# imports for guided decoding tests
+import re
+
+import openai
+import pytest
+
+from ...utils import RemoteOpenAIServer
+
+
+@pytest.mark.asyncio
+async def test_empty_prompt():
+    model_name = "gpt2"
+    server_args = ["--enforce-eager"]
+    with RemoteOpenAIServer(model_name, server_args) as remote_server:
+        client = remote_server.get_async_client()
+
+        with pytest.raises(openai.BadRequestError,
+                           match=re.compile('.+Prompt cannot be empty.+')):
+            await client.completions.create(model=model_name,
+                                            prompt="",
+                                            max_tokens=5,
+                                            temperature=0.0)
diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py
index 3526c3f6e898e..84eb778cd2a22 100644
--- a/vllm/engine/llm_engine.py
+++ b/vllm/engine/llm_engine.py
@@ -594,6 +594,7 @@ def _add_processed_request(
         prompt_adapter_request: Optional[PromptAdapterRequest],
         trace_headers: Optional[Mapping[str, str]] = None,
     ) -> None:
+        self._validate_model_inputs(processed_inputs)
         # Create the sequences.
         block_size = self.cache_config.block_size
         seq_id = next(self.seq_counter)
@@ -1691,3 +1692,10 @@ def is_encoder_decoder_model(self):
 
     def is_embedding_model(self):
         return self.model_config.is_embedding_model
+
+    def _validate_model_inputs(self, inputs: Union[LLMInputs,
+                                                   EncoderDecoderLLMInputs]):
+        prompt_key = "encoder_prompt_token_ids" \
+            if self.is_encoder_decoder_model() else "prompt_token_ids"
+        if not inputs.get(prompt_key):
+            raise ValueError("Prompt cannot be empty")