diff --git a/src/khoj/processor/conversation/prompts.py b/src/khoj/processor/conversation/prompts.py
index fcf30d07..29a04345 100644
--- a/src/khoj/processor/conversation/prompts.py
+++ b/src/khoj/processor/conversation/prompts.py
@@ -519,12 +519,13 @@ Q: {query}
 
 extract_questions_system_prompt = PromptTemplate.from_template(
     """
-You are Khoj, an extremely smart and helpful document search assistant with only the ability to retrieve information from the user's notes.
-Construct search queries to retrieve relevant information to answer the user's question.
+You are Khoj, an extremely smart and helpful document search assistant with only the ability to use natural language semantic search to retrieve information from the user's notes.
+Construct upto {max_queries} search queries to retrieve relevant information to answer the user's question.
 - You will be provided past questions(User), search queries(Assistant) and answers(A) for context.
-- Add as much context from the previous questions and answers as required into your search queries.
-- Break your search down into multiple search queries from a diverse set of lenses to retrieve all related documents.
-- Add date filters to your search queries from questions and answers when required to retrieve the relevant information.
+- You can use context from previous questions and answers to improve your search queries.
+- Break down your search into multiple search queries from a diverse set of lenses to retrieve all related documents. E.g who, what, where, when, why, how.
+- Add date filters to your search queries when required to retrieve the relevant information. This is the only structured query filter you can use.
+- Output 1 concept per query. Do not use boolean operators (OR/AND) to combine queries. They do not work and degrade search quality.
 - When asked a meta, vague or random questions, search for a variety of broad topics to answer the user's question.
 {personality_context}
 What searches will you perform to answer the users question? Respond with a JSON object with the key "queries" mapping to a list of searches you would perform on the user's knowledge base. Just return the queries and nothing else.
@@ -535,22 +536,27 @@ User's Location: {location}
 
 Here are some examples of how you can construct search queries to answer the user's question:
 
+Illustrate - Using diverse perspectives to retrieve all relevant documents
 User: How was my trip to Cambodia?
 Assistant: {{"queries": ["How was my trip to Cambodia?", "Angkor Wat temple visit", "Flight to Phnom Penh", "Expenses in Cambodia", "Stay in Cambodia"]}}
 A: The trip was amazing. You went to the Angkor Wat temple and it was beautiful.
 
+Illustrate - Combining date filters with natural language queries to retrieve documents in relevant date range
 User: What national parks did I go to last year?
 Assistant: {{"queries": ["National park I visited in {last_new_year} dt>='{last_new_year_date}' dt<'{current_new_year_date}'"]}}
 A: You visited the Grand Canyon and Yellowstone National Park in {last_new_year}.
 
+Illustrate - Using broad topics to answer meta or vague questions
 User: How can you help me?
 Assistant: {{"queries": ["Social relationships", "Physical and mental health", "Education and career", "Personal life goals and habits"]}}
 A: I can help you live healthier and happier across work and personal life
 
+Illustrate - Combining location and date in natural language queries with date filters to retrieve relevant documents
 User: Who all did I meet here yesterday?
 Assistant: {{"queries": ["Met in {location} on {yesterday_date} dt>='{yesterday_date}' dt<'{current_date}'"]}}
 A: Yesterday's note mentions your visit to your local beach with Ram and Shyam.
 
+Illustrate - Combining broad, diverse topics with date filters to answer meta or vague questions
 User: Share some random, interesting experiences from this month
 Assistant: {{"queries": ["Exciting travel adventures from {current_month}", "Fun social events dt>='{current_month}-01' dt<'{current_date}'", "Intense emotional experiences in {current_month}"]}}
 A: You had a great time at the local beach with your friends, attended a music concert and had a deep conversation with your friend, Khalid.
diff --git a/src/khoj/routers/helpers.py b/src/khoj/routers/helpers.py
index 8fce03aa..1c3394fa 100644
--- a/src/khoj/routers/helpers.py
+++ b/src/khoj/routers/helpers.py
@@ -1264,6 +1264,7 @@ async def extract_questions(
     location_data: LocationData = None,
     query_images: Optional[List[str]] = None,
     query_files: str = None,
+    max_queries: int = 5,
     tracer: dict = {},
 ):
     """
@@ -1293,14 +1294,20 @@ async def extract_questions(
         location=location,
         username=username,
         personality_context=personality_context,
+        max_queries=max_queries,
     )
 
     prompt = prompts.extract_questions_user_message.format(text=query, chat_history=chat_history_str)
 
     class DocumentQueries(BaseModel):
-        """Choose searches to run on user documents."""
+        """Choose semantic search queries to run on user documents."""
 
-        queries: List[str] = Field(..., min_items=1, description="List of search queries to run on user documents.")
+        queries: List[str] = Field(
+            ...,
+            min_length=1,
+            max_length=max_queries,
+            description="List of semantic search queries to run on user documents.",
+        )
 
     raw_response = await send_message_to_model_wrapper(
         system_message=system_prompt,