diff --git a/sage/chat.py b/sage/chat.py
index 3ae10a1..51ffb41 100644
--- a/sage/chat.py
+++ b/sage/chat.py
@@ -1,8 +1,3 @@
-"""A gradio app that enables users to chat with their codebase.
-
-You must run `sage-index $GITHUB_REPO` first in order to index the codebase into a vector store.
-"""
-
 import logging
 
 import configargparse
@@ -25,52 +20,44 @@ def build_rag_chain(args):
     llm = build_llm_via_langchain(args.llm_provider, args.llm_model)
     retriever = build_retriever_from_args(args)
 
-    # Prompt to contextualize the latest query based on the chat history.
-    contextualize_q_system_prompt = (
-        "Given a chat history and the latest user question which might reference context in the chat history, "
-        "formulate a standalone question which can be understood without the chat history. Do NOT answer the question, "
-        "just reformulate it if needed and otherwise return it as is."
+    contextualize_q_prompt = ChatPromptTemplate.from_messages([
+        ("system", (
+            "Given a chat history and the latest user question which might reference context in the chat history, "
+            "formulate a standalone question which can be understood without the chat history. "
+            "Do NOT answer the question, just reformulate it if needed and otherwise return it as is."
+        )),
+        MessagesPlaceholder("chat_history"),
+        ("human", "{input}")
+    ])
+
+    history_aware_retriever = create_history_aware_retriever(
+        llm.with_config(tags=["contextualize_q_llm"]),
+        retriever,
+        contextualize_q_prompt
     )
-    contextualize_q_prompt = ChatPromptTemplate.from_messages(
-        [
-            ("system", contextualize_q_system_prompt),
-            MessagesPlaceholder("chat_history"),
-            ("human", "{input}"),
-        ]
-    )
-    contextualize_q_llm = llm.with_config(tags=["contextualize_q_llm"])
-    history_aware_retriever = create_history_aware_retriever(contextualize_q_llm, retriever, contextualize_q_prompt)
 
     qa_system_prompt = (
-        f"You are my coding buddy, helping me quickly understand a GitHub repository called {args.repo_id}."
+        f"You are my coding buddy, helping me quickly understand a GitHub repository called {args.repo_id}. "
         "Assume I am an advanced developer and answer my questions in the most succinct way possible."
-        "\n\n"
-        "Here are some snippets from the codebase."
-        "\n\n"
-        "{context}"
+        "\n\nHere are some snippets from the codebase.\n\n{context}"
     )
-    qa_prompt = ChatPromptTemplate.from_messages(
-        [
+
+    question_answer_chain = create_stuff_documents_chain(
+        llm, ChatPromptTemplate.from_messages([
             ("system", qa_system_prompt),
             MessagesPlaceholder("chat_history"),
-            ("human", "{input}"),
-        ]
+            ("human", "{input}")
+        ])
     )
 
-    question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
-    rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
-    return rag_chain
+    return create_retrieval_chain(history_aware_retriever, question_answer_chain)
 
 
 def main():
     parser = configargparse.ArgParser(
         description="Batch-embeds a GitHub repository and its issues.", ignore_unknown_config_file_keys=True
     )
-    parser.add(
-        "--share",
-        default=False,
-        help="Whether to make the gradio app publicly accessible.",
-    )
+    parser.add("--share", default=False, help="Make the Gradio app publicly accessible.")
 
     validator = sage_config.add_all_args(parser)
     args = parser.parse_args()
@@ -79,39 +66,25 @@ def main():
     rag_chain = build_rag_chain(args)
 
     def source_md(file_path: str, url: str) -> str:
-        """Formats a context source in Markdown."""
+        """Format a context source in Markdown."""
         return f"[{file_path}]({url})"
 
     async def _predict(message, history):
-        """Performs one RAG operation."""
-        history_langchain_format = []
-        for human, ai in history:
-            history_langchain_format.append(HumanMessage(content=human))
-            history_langchain_format.append(AIMessage(content=ai))
-        history_langchain_format.append(HumanMessage(content=message))
-
-        query_rewrite = ""
-        response = ""
-        async for event in rag_chain.astream_events(
-            {
-                "input": message,
-                "chat_history": history_langchain_format,
-            },
-            version="v1",
-        ):
-            if event["name"] == "retrieve_documents" and "output" in event["data"]:
-                sources = [(doc.metadata["file_path"], doc.metadata["url"]) for doc in event["data"]["output"]]
-                # Deduplicate while preserving the order.
-                sources = list(dict.fromkeys(sources))
-                response += "## Sources:\n" + "\n".join([source_md(s[0], s[1]) for s in sources]) + "\n## Response:\n"
+        """Perform one RAG operation."""
+        history_langchain_format = [
+            (HumanMessage(content=human), AIMessage(content=ai)) for human, ai in history
+        ] + [HumanMessage(content=message)]
 
+        response, query_rewrite = "", ""
+        async for event in rag_chain.astream_events({"input": message, "chat_history": history_langchain_format}, version="v1"):
+            if event["name"] == "retrieve_documents" and "output" in event["data"]:
+                sources = {(doc.metadata["file_path"], doc.metadata["url"]) for doc in event["data"]["output"]}
+                response += "## Sources:\n" + "\n".join(source_md(*s) for s in sources) + "\n## Response:\n"
             elif event["event"] == "on_chat_model_stream":
                 chunk = event["data"]["chunk"].content
-
                 if "contextualize_q_llm" in event["tags"]:
                     query_rewrite += chunk
                 else:
-                    # This is the actual response to the user query.
                     if not response:
                         logging.info(f"Query rewrite: {query_rewrite}")
                     response += chunk
@@ -119,8 +92,8 @@ async def _predict(message, history):
 
     gr.ChatInterface(
         _predict,
-        title=args.repo_id,
-        examples=["What does this repo do?", "Give me some sample code."],
+        title=f"{args.repo_id}" if args.repo_id else "GitHub Repo Chat",
+        examples=["What does this repo do?", "Give me some sample code."]
     ).launch(share=args.share)