diff --git a/eval/anthropic_runner.py b/eval/anthropic_runner.py
index f8bb1b7..c759f1c 100644
--- a/eval/anthropic_runner.py
+++ b/eval/anthropic_runner.py
@@ -77,8 +77,6 @@ def run_anthropic_eval(args):
                 row = input_rows[i]
                 result_dict = f.result()
                 query_gen = result_dict["query"]
-                print("Query for")
-                print(query_gen)
                 reason = result_dict["reason"]
                 err = result_dict["err"]
                 # save custom metrics
@@ -142,9 +140,6 @@ def run_anthropic_eval(args):
             os.makedirs(output_dir)
         output_df.to_csv(output_file, index=False, float_format="%.2f")
 
-        # get average rate of exact matches
-        avg_acc = output_df["exact_match"].sum() / len(output_df)
-        print(f"Average rate of exact match: {avg_acc:.2f}")
         # get average rate of correct results
         avg_subset = output_df["correct"].sum() / len(output_df)
         print(f"Average correct rate: {avg_subset:.2f}")
diff --git a/query_generators/anthropic.py b/query_generators/anthropic.py
index 5cb77c1..b03f824 100644
--- a/query_generators/anthropic.py
+++ b/query_generators/anthropic.py
@@ -6,6 +6,7 @@
 
 from query_generators.query_generator import QueryGenerator
 from utils.pruning import prune_metadata_str
+from utils.gen_prompt import to_prompt_schema
 
 anthropic = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
 
@@ -95,18 +96,30 @@ def generate_query(
         self.query = ""
         self.reason = ""
 
+        if self.use_public_data:
+            from defog_data.metadata import dbs
+        else:
+            # raise Exception("Replace this with your private data import")
+            from defog_data_private.metadata import dbs
+
         with open(self.prompt_file) as file:
             model_prompt = file.read()
         question_instructions = question + " " + instructions
         if table_metadata_string == "":
-            pruned_metadata_ddl, join_str = prune_metadata_str(
-                question_instructions,
-                self.db_name,
-                self.use_public_data,
-                columns_to_keep,
-                shuffle,
-            )
-            pruned_metadata_str = pruned_metadata_ddl + join_str
+            if columns_to_keep > 0:
+                pruned_metadata_ddl, join_str = prune_metadata_str(
+                    question_instructions,
+                    self.db_name,
+                    self.use_public_data,
+                    columns_to_keep,
+                    shuffle,
+                )
+                pruned_metadata_str = pruned_metadata_ddl + join_str
+            elif columns_to_keep == 0:
+                md = dbs[self.db_name]["table_metadata"]
+                pruned_metadata_str = to_prompt_schema(md, shuffle)
+            else:
+                raise ValueError("columns_to_keep must be >= 0")
         else:
             pruned_metadata_str = table_metadata_string
         prompt = model_prompt.format(