diff --git a/readme_ready/query/create_chat_chain.py b/readme_ready/query/create_chat_chain.py
index fade4c6..71ccc73 100644
--- a/readme_ready/query/create_chat_chain.py
+++ b/readme_ready/query/create_chat_chain.py
@@ -149,7 +149,32 @@ def make_qa_chain(
     device: str = "cpu",
     on_token_stream=None,
 ):
-    """Make QA Chain"""
+    """
+    Creates a question-answering (QA) chain for the specified project.
+
+        Initializes and configures the QA chain using the provided repository and user configurations.
+        Selects the appropriate language model (LLM), sets up the retriever with a history-aware mechanism,
+        and combines document chains for processing queries. The chain facilitates interaction with the
+        vector store to retrieve and process relevant information based on user queries.
+
+        Args:
+            project_name: The name of the project for which the QA chain is being created.
+            repository_url: The URL of the repository containing the project.
+            content_type: The type of content to be processed (e.g., 'code', 'documentation').
+            chat_prompt: The prompt template used for generating chat responses.
+            target_audience: The intended audience for the QA responses.
+            vectorstore: An instance of HNSWLib representing the vector store containing document embeddings.
+            llms: A list of LLMModels to select from for generating embeddings and responses.
+            device: The device to use for model inference (default is 'cpu').
+            on_token_stream: Optional callback for handling token streams during model inference.
+
+        Returns:
+            A retrieval chain configured for question-answering, combining the retriever and document processing chain.
+
+        Raises:
+            ValueError: If no suitable model is found in the provided LLMs.
+            RuntimeError: If there is an issue initializing the chat models or creating the chains.
+    """
     llm = llms[1] if len(llms) > 1 else llms[0]
     llm_name = llm.value
     print(f"LLM:  {llm_name.lower()}")
@@ -240,7 +265,33 @@ def make_readme_chain(
     device: str = "cpu",
     on_token_stream=None,
 ):
-    """Make Readme Chain"""
+    """
+    Creates a README generation chain for the specified project.
+
+        Initializes and configures the README generation chain using the provided repository, user, and README configurations.
+        Selects the appropriate language model (LLM), sets up the document processing chain with the specified prompts,
+        and integrates with the vector store to generate comprehensive README sections based on project data.
+        The chain facilitates automated generation of README files tailored to the project's specifications.
+
+        Args:
+            project_name: The name of the project for which the README is being generated.
+            repository_url: The URL of the repository containing the project.
+            content_type: The type of content to be included in the README (e.g., 'overview', 'installation').
+            chat_prompt: The prompt template used for generating README content.
+            target_audience: The intended audience for the README.
+            vectorstore: An instance of HNSWLib representing the vector store containing document embeddings.
+            llms: A list of LLMModels to select from for generating README content.
+            peft_model: An optional parameter specifying a PEFT (Parameter-Efficient Fine-Tuning) model for enhanced performance.
+            device: The device to use for model inference (default is 'cpu').
+            on_token_stream: Optional callback for handling token streams during model inference.
+
+        Returns:
+            A retrieval chain configured for README generation, combining the retriever and document processing chain.
+
+        Raises:
+            ValueError: If no suitable model is found in the provided LLMs.
+            RuntimeError: If there is an issue initializing the chat models or creating the chains.
+    """
     llm = llms[1] if len(llms) > 1 else llms[0]
     llm_name = llm.value
     doc_chat_model = None