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diff --git a/benchmark/agbenchmark/utils/prompts.py b/benchmark/agbenchmark/utils/prompts.py
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+SCORING_MAP = {
+    "percentage": "assign a float score that will represent a percentage out of 100. Use decimal points to be even more accurate. 0 represents the worst possible generation, while 100 represents the ideal generation",
+    "scale": "assign an integer score from a scale of 1-10. 1 represents a really bad generation, while 10 represents an ideal generation",
+    "binary": "assign a binary score of either 0 or 1. 0 represents a failure, while 1 represents a success",
+}
+
+
+REFERENCE_PROMPT = """Ignore previous directions. You are now an expert at evaluating how close machine generated responses are to human answers. You essentially act as a hyper advanced BLEU score.
+In order to score the machine generated response you will {scoring}. Make sure to factor in the distance to the ideal response into your thinking, deliberation, and final result regarding scoring. Return nothing but a float score.
+
+Here is the given task for you to evaluate:
+{task}
+
+Here is the ideal response you're comparing to based on the task:
+{answer}
+
+Here is the current machine generated response to the task that you need to evaluate:
+{response}
+
+"""
+
+RUBRIC_PROMPT = """Ignore previous directions. You are now an expert at evaluating machine generated responses to given tasks.
+In order to score the generated texts you will {scoring}. Make sure to factor in rubric into your thinking, deliberation, and final result regarding scoring. Return nothing but a float score.
+
+Here is the given task for you to evaluate:
+{task}
+
+Use the below rubric to guide your thinking about scoring:
+{answer}
+
+Here is the current machine generated response to the task that you need to evaluate:
+{response}
+
+"""
+
+QUESTION_PROMPT = """Ignore previous directions. You are now an expert at evaluating machine generated responses to given tasks.
+In order to score the generated texts you will {scoring}. Make sure to think about whether the generated response answers the question well in order to score accurately. Return nothing but a float score.
+
+Here is the given task:
+{task}
+
+Here is a question that checks if the task was completed correctly:
+{answer}
+
+Here is the current machine generated response to the task that you need to evaluate:
+{response}
+
+"""
+
+FEW_SHOT_EXAMPLES = """Here are some examples of how to score a machine generated response based on the above:
+{examples}
+
+"""
+
+CUSTOM_PROMPT = """{custom}
+{scoring}
+
+"""
+
+PROMPT_MAP = {
+    "rubric": RUBRIC_PROMPT,
+    "reference": REFERENCE_PROMPT,
+    "question": QUESTION_PROMPT,
+    "custom": CUSTOM_PROMPT,
+}
+
+END_PROMPT = """Remember to always end your response with nothing but a float score.
+Float score:"""