diff --git a/py/autoevals/ragas.py b/py/autoevals/ragas.py
index 9a6d8a5..3ec690d 100644
--- a/py/autoevals/ragas.py
+++ b/py/autoevals/ragas.py
@@ -681,6 +681,26 @@ def extract_context_precision_request(question, answer, context, **extra_args):
     )
 
 
+def _calculate_context_precision_score(verdicts: list) -> float:
+    """Calculate RAGAS ContextPrecision score based on position of relevant chunks.
+
+    Score = sum(precision_at_k * verdict_k) / total_relevant
+    where precision_at_k = relevant chunks up to position k / k
+    """
+    total_relevant = sum(verdicts)
+    if total_relevant == 0:
+        return 0.0
+
+    score = 0.0
+    relevant_so_far = 0
+    for k, verdict in enumerate(verdicts, start=1):
+        if verdict == 1:
+            relevant_so_far += 1
+            score += relevant_so_far / k
+
+    return score / total_relevant
+
+
 class ContextPrecision(OpenAILLMScorer):
     """Measures how precise and focused the context is for answering the question.
 
@@ -730,31 +750,55 @@ def _postprocess(self, response):
     async def _run_eval_async(self, output, expected=None, input=None, context=None, **kwargs):
         check_required("ContextPrecision", input=input, expected=expected, context=context)
 
-        if isinstance(context, list):
-            context = "\n".join(context)
+        if not isinstance(context, list):
+            context = [context]
 
-        return self._postprocess(
-            await arun_cached_request(
+        # Score each context chunk individually
+        verdicts = []
+        for chunk in context:
+            response = await arun_cached_request(
                 client=self.client,
                 **extract_context_precision_request(
-                    question=input, answer=expected, context=context, model=self.model, **self.extra_args
+                    question=input, answer=expected, context=chunk, model=self.model, **self.extra_args
                 ),
             )
+            precision = json.loads(response["choices"][0]["message"]["tool_calls"][0]["function"]["arguments"])
+            verdicts.append(precision["verdict"])
+
+        # Apply RAGAS positional formula
+        score = _calculate_context_precision_score(verdicts)
+
+        return Score(
+            name=self._name(),
+            score=score,
+            metadata={"verdicts": verdicts},
         )
 
     def _run_eval_sync(self, output, expected=None, input=None, context=None, **kwargs):
         check_required("ContextPrecision", input=input, expected=expected, context=context)
 
-        if isinstance(context, list):
-            context = "\n".join(context)
+        if not isinstance(context, list):
+            context = [context]
 
-        return self._postprocess(
-            run_cached_request(
+        # Score each context chunk individually
+        verdicts = []
+        for chunk in context:
+            response = run_cached_request(
                 client=self.client,
                 **extract_context_precision_request(
-                    question=input, answer=expected, context=context, model=self.model, **self.extra_args
+                    question=input, answer=expected, context=chunk, model=self.model, **self.extra_args
                 ),
             )
+            precision = json.loads(response["choices"][0]["message"]["tool_calls"][0]["function"]["arguments"])
+            verdicts.append(precision["verdict"])
+
+        # Apply RAGAS positional formula
+        score = _calculate_context_precision_score(verdicts)
+
+        return Score(
+            name=self._name(),
+            score=score,
+            metadata={"verdicts": verdicts},
         )