diff --git a/src/backend/chat.py b/src/backend/chat.py
index 24981da..257e73c 100644
--- a/src/backend/chat.py
+++ b/src/backend/chat.py
@@ -4,6 +4,7 @@
 
 from fastapi import HTTPException
 from llama_index.core.llms import LLM
+from llama_index.llms.azure_openai import AzureOpenAI
 from llama_index.llms.groq import Groq
 from llama_index.llms.ollama import Ollama
 from llama_index.llms.openai import OpenAI
@@ -45,8 +46,25 @@ def rephrase_query_with_history(question: str, history: List[Message], llm: LLM)
         )
 
 
+def get_openai_model(model: ChatModel) -> LLM:
+    openai_mode = os.environ.get("OPENAI_MODE", "openai")
+    if openai_mode == "azure":
+        return AzureOpenAI(
+            deployment_name=os.environ.get("AZURE_DEPLOYMENT_NAME"),
+            api_key=os.environ.get("AZURE_API_KEY"),
+            azure_endpoint=os.environ.get("AZURE_CHAT_ENDPOINT"),
+            api_version="2024-04-01-preview",
+        )
+    elif openai_mode == "openai":
+        return OpenAI(model=model_mappings[model])
+    else:
+        raise ValueError(f"Unknown model: {model}")
+
+
 def get_llm(model: ChatModel) -> LLM:
-    if model in [ChatModel.GPT_3_5_TURBO, ChatModel.GPT_4o]:
+    if model == ChatModel.GPT_3_5_TURBO:
+        return get_openai_model(model)
+    elif model == ChatModel.GPT_4o:
         return OpenAI(model=model_mappings[model])
     elif model in [
         ChatModel.LOCAL_GEMMA,
diff --git a/src/backend/prompts.py b/src/backend/prompts.py
index ee95a18..a49956c 100644
--- a/src/backend/prompts.py
+++ b/src/backend/prompts.py
@@ -27,10 +27,10 @@
 RELATED_QUESTION_PROMPT = """\
 Given a question and search result context, generate 3 follow-up questions the user might ask. Use the original question and context.
 
-There must be EXACTLY 3 questions. Keep the questions concise, and simple. This should return an object with the following fields:
-
-questions: A list of 3 concise, simple questions
-
+Instructions:
+- Generate exactly 3 questions.
+- These questions should be concise, and simple.
+- Ensure the follow-up questions are relevant to the original question and context.
 Make sure to match the language of the user's question.
 
 Original Question: {query}
@@ -38,7 +38,8 @@
 {context}
 </context>
 
-Your EXACTLY 3 (three) follow-up questions:
+Output:
+related_questions: A list of EXACTLY three concise, simple follow-up questions
 """
 
 HISTORY_QUERY_REPHRASE = """
diff --git a/src/backend/related_queries.py b/src/backend/related_queries.py
index 09f590e..3312391 100644
--- a/src/backend/related_queries.py
+++ b/src/backend/related_queries.py
@@ -15,8 +15,27 @@
 OLLAMA_HOST = os.environ.get("OLLAMA_HOST", "http://localhost:11434")
 
 
+def get_openai_client() -> openai.AsyncOpenAI:
+    openai_mode = os.environ.get("OPENAI_MODE", "openai")
+    if openai_mode == "openai":
+        return openai.AsyncOpenAI()
+    elif openai_mode == "azure":
+        return openai.AsyncAzureOpenAI(
+            azure_deployment=os.environ.get("AZURE_DEPLOYMENT_NAME"),
+            azure_endpoint=os.environ["AZURE_CHAT_ENDPOINT"],
+            api_key=os.environ.get("AZURE_API_KEY"),
+            api_version="2024-04-01-preview",
+        )
+    else:
+        raise ValueError(f"Unknown openai mode: {openai_mode}")
+
+
 def instructor_client(model: ChatModel) -> instructor.AsyncInstructor:
-    if model in [
+    if model == ChatModel.GPT_3_5_TURBO:
+        return instructor.from_openai(
+            get_openai_client(),
+        )
+    elif model in [
         ChatModel.GPT_3_5_TURBO,
         ChatModel.GPT_4o,
     ]:
@@ -35,7 +54,7 @@ def instructor_client(model: ChatModel) -> instructor.AsyncInstructor:
             mode=instructor.Mode.JSON,
         )
     elif model == ChatModel.LLAMA_3_70B:
-        return instructor.from_groq(groq.AsyncGroq(), mode=instructor.Mode.JSON)
+        return instructor.from_groq(groq.AsyncGroq(), mode=instructor.Mode.JSON)  # type: ignore
     else:
         raise ValueError(f"Unknown model: {model}")
 
@@ -50,6 +69,8 @@ async def generate_related_queries(
     client = instructor_client(model)
     model_name = model_mappings[model]
 
+    print(RELATED_QUESTION_PROMPT.format(query=query, context=context))
+
     related = await client.chat.completions.create(
         model=model_name,
         response_model=RelatedQueries,
@@ -61,4 +82,4 @@ async def generate_related_queries(
         ],
     )
 
-    return [query.lower().replace("?", "") for query in related.questions]
+    return [query.lower().replace("?", "") for query in related.related_questions]
diff --git a/src/backend/schemas.py b/src/backend/schemas.py
index 7b3f7ef..89d9406 100644
--- a/src/backend/schemas.py
+++ b/src/backend/schemas.py
@@ -37,7 +37,7 @@ class ChatRequest(BaseModel, plugin_settings=record_all):
 
 
 class RelatedQueries(BaseModel):
-    questions: List[str] = Field(..., min_length=3, max_length=3)
+    related_questions: List[str] = Field(..., min_length=3, max_length=3)
 
 
 class SearchResult(BaseModel):
diff --git a/src/frontend/src/components/model-selection.tsx b/src/frontend/src/components/model-selection.tsx
index d0d1da3..bb17f42 100644
--- a/src/frontend/src/components/model-selection.tsx
+++ b/src/frontend/src/components/model-selection.tsx
@@ -111,7 +111,7 @@ const ModelItem: React.FC<{ model: Model }> = ({ model }) => (
 );
 
 export function ModelSelection() {
-  const { model, setModel, localMode } = useConfigStore();
+  const { localMode, model, setModel } = useConfigStore();
 
   return (
     <Select