diff --git a/align_data/embeddings/embedding_utils.py b/align_data/embeddings/embedding_utils.py
index cf98228c..ff386325 100644
--- a/align_data/embeddings/embedding_utils.py
+++ b/align_data/embeddings/embedding_utils.py
@@ -88,34 +88,39 @@ def wrapper(*args, **kwargs):
 
 
 @handle_openai_errors
-def moderation_check(texts: List[str], max_texts_num: int = 32) -> List[ModerationInfoType]:
-    """
-    Check moderation on a list of texts.
+def _single_batch_moderation_check(batch: List[str]) -> List[ModerationInfoType]:
+    """Process a batch for moderation checks."""
+    return openai.Moderation.create(input=batch)["results"]
 
-    Parameters:
-    - texts (List[str]): List of texts to be checked for moderation.
-    - max_texts_num (int): Number of texts to check at once. Defaults to 32.
 
-    Returns:
-    - List[ModerationInfoType]: List of moderation results for the provided texts.
-    """
-    total_texts = len(texts)
-    results = []
+def moderation_check(texts: List[str], max_texts_num: int = 32) -> List[ModerationInfoType]:
+    """Batch moderation checks on list of texts."""
+    return [
+        result
+        for batch in (texts[i : i + max_texts_num] for i in range(0, len(texts), max_texts_num))
+        for result in _single_batch_moderation_check(batch)
+    ]
 
-    for i in range(0, total_texts, max_texts_num):
-        batch_texts = texts[i : i + max_texts_num]
-        batch_results = openai.Moderation.create(input=batch_texts)["results"]
-        results.extend(batch_results)
 
-    return results
+@handle_openai_errors
+def _single_batch_compute_openai_embeddings(batch: List[str], **kwargs) -> List[List[float]]:
+    """Compute embeddings for a batch."""
+    batch_data = openai.Embedding.create(input=batch, engine=OPENAI_EMBEDDINGS_MODEL, **kwargs).data
+    return [d["embedding"] for d in batch_data]
 
 
-@handle_openai_errors
-def _compute_openai_embeddings(non_flagged_texts: List[str], **kwargs) -> List[List[float]]:
-    data = openai.Embedding.create(
-        input=non_flagged_texts, engine=OPENAI_EMBEDDINGS_MODEL, **kwargs
-    ).data
-    return [d["embedding"] for d in data]
+def _compute_openai_embeddings(
+    non_flagged_texts: List[str], max_texts_num: int = 2048, **kwargs
+) -> List[List[float]]:
+    """Batch computation of embeddings for non-flagged texts."""
+    return [
+        embedding
+        for batch in (
+            non_flagged_texts[i : i + max_texts_num]
+            for i in range(0, len(non_flagged_texts), max_texts_num)
+        )
+        for embedding in _single_batch_compute_openai_embeddings(batch, **kwargs)
+    ]
 
 
 def get_embeddings_without_moderation(
@@ -193,7 +198,6 @@ def get_embeddings(
     Returns:
     - Tuple[List[Optional[List[float]]], ModerationInfoListType]: Tuple containing the list of embeddings (with None for flagged texts) and the moderation results.
     """
-    assert len(texts) <= 2048, "The batch size should not be larger than 2048."
     assert all(texts), "No empty strings allowed in the input list."
 
     # replace newlines, which can negatively affect performance