diff --git a/app.py b/app.py
index b0896eb..bb1b5bd 100644
--- a/app.py
+++ b/app.py
@@ -183,13 +183,19 @@ def grade_writing_task_1():
     try:
         data = request.get_json()
         question = data.get('question')
-        context = data.get('context')
         answer = data.get('answer')
         if has_words(answer):
-            messages = get_grading_messages(QuestionType.WRITING_TASK_1, question, answer, context)
-            token_count = reduce(lambda count, item: count + count_tokens(item)['n_tokens'],
-                                 map(lambda x: x["content"], filter(lambda x: "content" in x, messages)), 0)
-            response = make_openai_call(GPT_3_5_TURBO, messages, token_count, GRADING_FIELDS, GRADING_TEMPERATURE)
+            message = (
+                    "Grade this Writing Task 1 answer according to ielts grading system and provide an example of a perfect "
+                    "answer and an elaborated comment where you deep dive into what is wrong and right about the answer."
+                    "Provide your answer on the following json format: {'perfect_answer': 'example perfect answer', 'comment': 'comment about answer quality', 'overall': 7.0, "
+                    "'task_response': {'Task Achievement': 0.0, 'Coherence and Cohesion': 0.0, 'Lexical Resource': 0.0, "
+                    "'Grammatical Range and Accuracy': 0.0}}\n The question was '" + question + "' "
+                                                                                                "and the answer was '" + answer + "'")
+            token_count = count_tokens(message)["n_tokens"]
+            response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, message, token_count,
+                                                 ["comment"],
+                                                 GEN_QUESTION_TEMPERATURE)
             return response
         else:
             return {
@@ -231,10 +237,17 @@ def grade_writing_task_2():
         question = data.get('question')
         answer = data.get('answer')
         if has_words(answer):
-            messages = get_grading_messages(QuestionType.WRITING_TASK_2, question, answer)
-            token_count = reduce(lambda count, item: count + count_tokens(item)['n_tokens'],
-                                 map(lambda x: x["content"], filter(lambda x: "content" in x, messages)), 0)
-            response = make_openai_call(GPT_3_5_TURBO, messages, token_count, GRADING_FIELDS, GRADING_TEMPERATURE)
+            message = (
+                    "Grade this Writing Task 2 answer according to ielts grading system and provide an example of a perfect "
+                    "answer and an elaborated comment where you deep dive into what is wrong and right about the answer."
+                    "Provide your answer on the following json format: {'perfect_answer': 'example perfect answer', 'comment': 'comment about answer quality', 'overall': 7.0, "
+                    "'task_response': {'Task Achievement': 0.0, 'Coherence and Cohesion': 0.0, 'Lexical Resource': 0.0, "
+                    "'Grammatical Range and Accuracy': 0.0}}\n The question was '" + question + "' "
+                                                                                                "and the answer was '" + answer + "'")
+            token_count = count_tokens(message)["n_tokens"]
+            response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, message, token_count,
+                                                 ["comment"],
+                                                 GEN_QUESTION_TEMPERATURE)
             return response
         else:
             return {
@@ -597,6 +610,7 @@ def get_reading_passage_3_question():
     except Exception as e:
         return str(e)
 
+
 @app.route('/reading', methods=['POST'])
 @jwt_required()
 def save_reading_passage():