diff --git a/sentry_sdk/integrations/langchain.py b/sentry_sdk/integrations/langchain.py
index d19d9bbdd5..1d77001684 100644
--- a/sentry_sdk/integrations/langchain.py
+++ b/sentry_sdk/integrations/langchain.py
@@ -351,7 +351,6 @@ def on_llm_start(
         metadata: "Optional[Dict[str, Any]]" = None,
         **kwargs: "Any",
     ) -> "Any":
-        """Run when LLM starts running."""
         with capture_internal_exceptions():
             if not run_id:
                 return
@@ -369,12 +368,18 @@ def on_llm_start(
             watched_span = self._create_span(
                 run_id,
                 parent_run_id,
-                op=OP.GEN_AI_PIPELINE,
-                name=kwargs.get("name") or "Langchain LLM call",
+                op=OP.GEN_AI_GENERATE_TEXT,
+                name=f"generate_text {model}".strip(),
                 origin=LangchainIntegration.origin,
             )
             span = watched_span.span
 
+            span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "generate_text")
+
+            pipeline_name = kwargs.get("name")
+            if pipeline_name:
+                span.set_data(SPANDATA.GEN_AI_PIPELINE_NAME, pipeline_name)
+
             if model:
                 span.set_data(
                     SPANDATA.GEN_AI_REQUEST_MODEL,
diff --git a/tests/integrations/langchain/test_langchain.py b/tests/integrations/langchain/test_langchain.py
index 132da0a9a0..00feb36a50 100644
--- a/tests/integrations/langchain/test_langchain.py
+++ b/tests/integrations/langchain/test_langchain.py
@@ -851,12 +851,15 @@ def test_langchain_integration_with_langchain_core_only(sentry_init, capture_eve
         assert tx["type"] == "transaction"
 
         llm_spans = [
-            span for span in tx.get("spans", []) if span.get("op") == "gen_ai.pipeline"
+            span
+            for span in tx.get("spans", [])
+            if span.get("op") == "gen_ai.generate_text"
         ]
         assert len(llm_spans) > 0
 
         llm_span = llm_spans[0]
-        assert llm_span["description"] == "Langchain LLM call"
+        assert llm_span["description"] == "generate_text gpt-3.5-turbo"
+        assert llm_span["data"]["gen_ai.operation.name"] == "generate_text"
         assert llm_span["data"]["gen_ai.request.model"] == "gpt-3.5-turbo"
         assert (
             llm_span["data"]["gen_ai.response.text"]
@@ -1062,11 +1065,12 @@ def test_langchain_message_truncation(sentry_init, capture_events):
     assert tx["type"] == "transaction"
 
     llm_spans = [
-        span for span in tx.get("spans", []) if span.get("op") == "gen_ai.pipeline"
+        span for span in tx.get("spans", []) if span.get("op") == "gen_ai.generate_text"
     ]
     assert len(llm_spans) > 0
 
     llm_span = llm_spans[0]
+    assert llm_span["data"]["gen_ai.operation.name"] == "generate_text"
     assert SPANDATA.GEN_AI_REQUEST_MESSAGES in llm_span["data"]
 
     messages_data = llm_span["data"][SPANDATA.GEN_AI_REQUEST_MESSAGES]
@@ -1776,11 +1780,12 @@ def test_langchain_response_model_extraction(
     assert tx["type"] == "transaction"
 
     llm_spans = [
-        span for span in tx.get("spans", []) if span.get("op") == "gen_ai.pipeline"
+        span for span in tx.get("spans", []) if span.get("op") == "gen_ai.generate_text"
     ]
     assert len(llm_spans) > 0
 
     llm_span = llm_spans[0]
+    assert llm_span["data"]["gen_ai.operation.name"] == "generate_text"
 
     if expected_model is not None:
         assert SPANDATA.GEN_AI_RESPONSE_MODEL in llm_span["data"]