Prompts improved for all cards

app/ads1/budget_optimizer.py

@@ -41,10 +41,28 @@ def build_budget_optimizer_prompt(context: dict) -> str:
     name = context.get("campaign_name", "this account")
 
     return (
-        f"Write 3 to 5 bullet points of actionable Google Ads budget allocation 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}"
-    )
+        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}
+        """
+        )
+    
 
 def run_budget_optimizer(dfs: dict, campaign_name: str | None = None) -> str:
     print("\n🚀 [budget_optimizer] STARTED", flush=True)

app/ads1/growth_finder.py

@@ -41,16 +41,26 @@ def build_growth_finder_prompt(context: dict) -> str:
     name = context.get("campaign_name", "this account")
 
     return (
-        f"Write 3 to 5 bullet points of actionable Google Ads scaling opportunities for {name}.\n"
-        "Use simple language. One insight per bullet. Start each line with '- '. No intro sentence.\n\n"
-        "Focus on:\n"
-        "- keywords/ad groups that can scale budget profitably\n"
-        "- patterns that show strong conversion efficiency\n"
-        "- underfunded high-performing segments\n"
-        "- expansion opportunities (similar keywords, match types, themes)\n"
-        "- signals of demand that are not fully exploited\n\n"
-        "- Only suggest scaling where performance supports it\n"
-        f"Data (JSON):\n{payload}"
+        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}
+            """
     )
 
 def run_growth_finder(dfs: dict, campaign_name: str | None = None) -> str:

app/ads1/keyword_inspector.py

@@ -28,32 +28,25 @@ def build_keyword_prompt(context: dict) -> str:
     payload = json.dumps(context, indent=2, default=str)
 
     return f"""
-You are an expert Google Ads performance strategist for a preschool business.
+        You are a Google Ads keyword performance expert for preschool campaigns.
 
-Campaign: {context.get("campaign_name", "ALL CAMPAIGNS")}
+        TASK:
+        Classify keywords into 3–5 insights.
 
-Your job:
-Analyze keyword performance and identify:
-- Winning keywords (high intent + conversions)
-- Wasted spend keywords (cost but no conversions)
-- Keywords to scale
-- Keywords to pause or reduce bids
-- Any CTR / conversion anomalies
-
-IMPORTANT:
-- Do NOT assume CRM / SaaS context
-- Assume all data relates to preschool admissions, daycare, or childcare services
-- Think like a preschool marketing expert
-
-DATA:
-{payload}
-
-Return 5 bullet points.
-Each bullet must start with "- ".
-Be direct, business-focused, no intro text.
-"""
+        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}
+        """
 # -------------------------
 # Main runner
 # -------------------------

app/ads1/search_term_optimizer.py

@@ -44,22 +44,26 @@ def build_search_optimizer_prompt(context: dict) -> str:
     name = context.get("campaign_name", "this campaign")
 
     return (
-        f"You are an expert Google Ads search term optimization strategist.\n\n"
-        f"Analyze search term performance for {name}.\n\n"
-        "Your job is to provide 3 to 5 actionable insights.\n"
-        "Focus on:\n"
-        "- wasted spend search terms\n"
-        "- high intent converting terms\n"
-        "- scaling opportunities\n"
-        "- terms to pause or reduce bids\n"
-        "- unusual CTR / CPA / conversion patterns\n\n"
-        "Rules:\n"
-        "- Be specific and actionable\n"
-        "- Use simple language\n"
-        "- One insight per bullet point\n"
-        "- Start each line with '- '\n"
-        "- No intro sentence, no summary, no JSON\n\n"
-        f"Data:\n{payload}"
+        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.
+
+            OUTPUT FORMAT:
+            - Search Term: <term>
+            Category: Wasted Spend | High Intent | Scale | Negative Keyword Candidate
+            Evidence: <cost, clicks, conversions>
+            Action: <pause / scale / add as keyword / add as negative>
+
+            DATA:
+            {payload}
+            """
     )
 
 # -------------------------