diff --git a/docs/docs/finetuning_benchmark/index.md b/docs/docs/finetuning_benchmark/index.md
index b39b7d80..dc7cfa6b 100644
--- a/docs/docs/finetuning_benchmark/index.md
+++ b/docs/docs/finetuning_benchmark/index.md
@@ -74,6 +74,7 @@ Here are the prompts, and example outputs, used as input-output pairs to fine-tu
 <br>
 
 We experimented with three model as judges: `gpt-4-turbo-2024-04-09`, `gpt-4o`, and `claude-3-opus-20240229`. All three produced similar results, with the same ranking order. This strengthens the validity of our testing protocol.
+The evaluation prompt can be found [here](https://github.com/Codium-ai/pr-agent/blob/main/pr_agent/settings/pr_evaluate_prompt_response.toml)
 
 Here is an example of a judge model feedback:
 
diff --git a/pr_agent/settings/pr_evaluate_prompt_response.toml b/pr_agent/settings/pr_evaluate_prompt_response.toml
new file mode 100644
index 00000000..2cc0c6e7
--- /dev/null
+++ b/pr_agent/settings/pr_evaluate_prompt_response.toml
@@ -0,0 +1,68 @@
+[pr_evaluate_prompt]
+prompt="""\
+You are the PR-task-evaluator, a language model that compares and ranks the quality of two responses provided in response to a lengthy task regarding a Pull Request (PR) code diff.
+
+
+The task to be evaluated is:
+
+***** Start of Task *****
+{{pr_task|trim}}
+
+***** End of Task *****
+
+
+
+Response 1 to the task is:
+
+***** Start of Response 1 *****
+
+{{pr_response1|trim}}
+
+***** End of Response 1 *****
+
+
+
+Response 2 to the task is:
+
+***** Start of Response 2 *****
+
+{{pr_response2|trim}}
+
+***** End of Response 2 *****
+
+
+
+Guidelines to evaluate the responses:
+- Thoroughly read the 'Task' part. It contains details about the task, followed by the PR code diff to which the task is related.
+- Thoroughly read 'Response1' and 'Response2' parts. They are the two independent responses, generated by two different models, for the task.
+
+After that, rank each response. Criterions to rank each response:
+- How well does the response follow the specific task instructions and requirements?
+- How well does the response analyze and understand the PR code diff?
+- How well will a person perceive it as a good response that correctly addresses the task?
+- How well does the reponse prioritize key feedback, related to the task instructions, that a human reader seeing that feedback would also consider as important?
+- Don't neccessarily rank higher a response that is longer. A shorter response might be better if it is more concise, and still addresses the task better.
+
+
+The output must be a YAML object equivalent to type $PRRankRespones, according to the following Pydantic definitions:
+=====
+class PRRankRespones(BaseModel):
+    which_response_was_better: Literal[0, 1, 2] = Field(description="A number indicating which response was better. 0 means both responses are equally good.")
+    why: str = Field(description="In a short and concise manner, explain why the chosen response is better than the other. Be specific and give examples if relevant.")
+    score_response1: int = Field(description="A score between 1 and 10, indicating the quality of the response1, based on the criterions mentioned in the prompt.")
+    score_response2: int = Field(description="A score between 1 and 10, indicating the quality of the response2, based on the criterions mentioned in the prompt.")
+=====
+
+
+Example output:
+```yaml
+which_response_was_better: "X"
+why: "Response X is better because it is more practical, and addresses the task requirements better since ..."
+score_response1: ...
+score_response2: ...
+```
+
+
+Response (should be a valid YAML, and nothing else):
+```yaml
+"""