diff --git a/new-backend/app/modules/langgraph_nodes/judge.py b/new-backend/app/modules/langgraph_nodes/judge.py
index ddecb190..8ad862bf 100644
--- a/new-backend/app/modules/langgraph_nodes/judge.py
+++ b/new-backend/app/modules/langgraph_nodes/judge.py
@@ -1,23 +1,53 @@
+import re
+from langchain_groq import ChatGroq
+from langchain.schema import HumanMessage
+
+# Init once
+groq_llm = ChatGroq(
+    model="gemma2-9b-it",
+    temperature=0.0,
+    max_tokens=10,
+)
+
+
 def judge_perspective(state):
-    # Dummy scoring
     try:
-        perspective = state.get("perspective")
+        perspective_obj = state.get("perspective")
+        text = getattr(perspective_obj, "perspective", "").strip()
+        if not text:
+            raise ValueError("Empty 'perspective' for scoring")
+
+        prompt = f"""
+You are an expert evaluator. Please rate the following counter-perspective
+on originality, reasoning quality, and factual grounding. Provide ONLY
+a single integer score from 0 (very poor) to 100 (excellent).
+
+=== Perspective to score ===
+{text}
+"""
 
-        if not perspective:
-            raise ValueError("Missing or empty 'perspective' in state")
+        response = groq_llm.invoke([HumanMessage(content=prompt)])
+
+        if isinstance(response, list) and response:
+            raw = response[0].content.strip()
+        elif hasattr(response, "content"):
+            raw = response.content.strip()
+        else:
+            raw = str(response).strip()
+
+        # 5) Pull the first integer 0–100
+        m = re.search(r"\b(\d{1,3})\b", raw)
+        if not m:
+            raise ValueError(f"Couldn’t parse a score from: '{raw}'")
+
+        score = max(0, min(100, int(m.group(1))))
+
+        return {**state, "score": score, "status": "success"}
 
-        score = 85 if "reasoning" in perspective else 40
     except Exception as e:
-        print(f"some error occured in judge_perspetive:{e}")
+        print(f"Error in judge_perspective: {e}")
         return {
             "status": "error",
             "error_from": "judge_perspective",
-            "message": f"{e}",
-            }
-    return {
-        **state,
-        "score": score,
-        "status": "success"
+            "message": str(e),
         }
-
-# llm based score assignment
diff --git a/new-backend/app/utils/fact_check_utils.py b/new-backend/app/utils/fact_check_utils.py
index caa356c0..8d5b446e 100644
--- a/new-backend/app/utils/fact_check_utils.py
+++ b/new-backend/app/utils/fact_check_utils.py
@@ -9,9 +9,9 @@ def run_fact_check_pipeline(state):
 
     result = run_claim_extractor_sdk(state)
 
-
     if state["status"] != "success":
         return result
+
     # Step 1: Extract claims
     raw_output = result["verifiable_claims"]
 
@@ -31,7 +31,6 @@ def run_fact_check_pipeline(state):
             print(f"❌ Search failed for: {claim} -> {e}")
         time.sleep(4)  # Add 4 second delay to prevent rate-limit
 
-
     if not search_results:
         return [], "All claim searches failed or returned no results."