Prompts shortened

app/ads1/budget_optimizer.py

@@ -41,27 +41,10 @@ def build_budget_optimizer_prompt(context: dict) -> str:
     name = context.get("campaign_name", "this account")
 
     return (
-        f"""
-        You are a Google Ads budget optimizer.
-
-        TASK:
-        Identify 3–5 budget allocation actions for the campaign {name}.
-
-        STRICT RULES:
-        - Use only provided data.
-        - Do not explain categories (no "keywords/ad groups...")
-        - No repetition of headings or prompt structure.
-        - Avoid generic advice.
-
-        OUTPUT FORMAT:
-        - Action: <what to change in budget>
-        Evidence: <metrics>
-        Expected impact: <why it helps>
-
-        DATA:
-        {payload}
-        """
-        )
+        f"Write 3 to 5 bullet points of actionable Budget optimization insights for {name}.\n"
+        "Use simple language. One insight per bullet. Start each line with '- '. No intro sentence.\n\n"
+        f"Data (JSON):\n{payload}"
+    )
     
 
 def run_budget_optimizer(dfs: dict, campaign_name: str | None = None) -> str:

app/ads1/growth_finder.py

@@ -41,25 +41,10 @@ def build_growth_finder_prompt(context: dict) -> str:
     name = context.get("campaign_name", "this account")
 
     return (
-        f"""
-            You are a Google Ads growth strategist.
-
-            TASK:
-            Identify ONLY data-backed scaling opportunities.
-
-            STRICT RULES:
-            - If performance is weak, DO NOT suggest scaling.
-            - Do not infer missing ad groups or structures.
-            - No generic marketing theory.
-
-            OUTPUT FORMAT:
-            - Opportunity: <keyword / segment>
-            Evidence: <CTR, conversions, CPA>
-            Why it works: <short justification>
-            Action: <scale suggestion (budget / bids / match type expansion)>
-
-            DATA:
-            {payload}
+            f"""
+                Identify ONLY data-backed scaling opportunities.Suggest opportunities for growth, with evidence and action.
+                DATA:
+                {payload}
             """
     )
 

app/ads1/keyword_inspector.py

@@ -28,24 +28,11 @@ def build_keyword_prompt(context: dict) -> str:
     payload = json.dumps(context, indent=2, default=str)
 
     return f"""
-        You are a Google Ads keyword performance expert for preschool campaigns.
-
-        TASK:
-        Classify keywords into 3–5 insights.
-
-        STRICT RULES:
-        - Do NOT invent ad groups or missing fields.
-        - Only use given keyword metrics.
-        - No paragraphs. Only bullet format.
-
-        OUTPUT FORMAT:
-        - Keyword: <name>
-        Label: Winning | Wasted Spend | Scale | Reduce | Investigate
-        Evidence: <CTR, cost, conversions>
-        Action: <specific bid or pause action>
-
-        DATA:
-        {payload}
+        Write 3 to 5 bullet points of actionable keyword performance insights.\n"
+        "Analyze individual keywords from the data and classify them as winning, 
+        wasted spend, or scaling opportunities using CTR, cost, and conversions. Add reasoning.
+        "Use simple language. One insight per bullet. Start each line with '- '. No intro sentence.\n\n"
+        f"Data (JSON):\n{payload}"
         """
 # -------------------------
 # Main runner

app/ads1/search_term_optimizer.py

@@ -45,15 +45,7 @@ def build_search_optimizer_prompt(context: dict) -> str:
 
     return (
         f"""
-            You are a search term optimization specialist.
-
-            TASK:
-            Return 3–5 search term actions.
-
-            STRICT RULES:
-            - Only use provided search terms.
-            - Do not compare unrelated terms.
-            - No explanations longer than 1 sentence per bullet.
+            Return 3–5 search term actions for given campaign data.
 
             OUTPUT FORMAT:
             - Search Term: <term>