diff --git a/docs/howtos/applications/data_preparation.md b/docs/howtos/applications/data_preparation.md
index 65e549802..619bb874b 100644
--- a/docs/howtos/applications/data_preparation.md
+++ b/docs/howtos/applications/data_preparation.md
@@ -10,7 +10,7 @@ This tutorial assumes that you have the 4 required data points from your RAG pip
 1. Question: A set of questions. 
 2. Contexts: Retrieved contexts corresponding to each question. This is a `list[list]` since each question can retrieve multiple text chunks.
 3. Answer: Generated answer corresponding to each question.
-4. Ground truths: Ground truths corresponding to each question. This is also a `list[list]` since each question may have multiple ground truths. 
+4. Ground truths: Ground truths corresponding to each question. This is a `str` which corresponds to the expected answer for each question.
 
 
 ## Example dataset
@@ -24,7 +24,7 @@ data_samples = {
     'answer': ['The first superbowl was held on January 15, 1967', 'The most super bowls have been won by The New England Patriots'],
     'contexts' : [['The Super Bowl....season since 1966,','replacing the NFL...in February.'], 
     ['The Green Bay Packers...Green Bay, Wisconsin.','The Packers compete...Football Conference']],
-    'ground_truth': [['The first superbowl was held on January 15, 1967'], ['The New England Patriots have won the Super Bowl a record six times']]
+    'ground_truth': ['The first superbowl was held on January 15, 1967', 'The New England Patriots have won the Super Bowl a record six times']
 }
 dataset = Dataset.from_dict(data_samples)
 ```
\ No newline at end of file
diff --git a/docs/howtos/customisations/bring-your-own-llm-or-embs.md b/docs/howtos/customisations/bring-your-own-llm-or-embs.md
index abc92945c..33ca8b4b5 100644
--- a/docs/howtos/customisations/bring-your-own-llm-or-embs.md
+++ b/docs/howtos/customisations/bring-your-own-llm-or-embs.md
@@ -3,7 +3,7 @@
 Ragas uses LLMs and Embeddings for both evaluation and test set generation. By default, the LLM and Embedding models of choice are OpenAI models.
 
 - [Evaluations](#evaluations)
-- [Testset Generation](#testset-generation)
+- [Testset Generation](#test-set-generation)
 
 
 :::{note}