diff --git a/src/promptflow-evals/promptflow/evals/evaluate/_evaluate.py b/src/promptflow-evals/promptflow/evals/evaluate/_evaluate.py
index 9437541d082..4fd030eaa47 100644
--- a/src/promptflow-evals/promptflow/evals/evaluate/_evaluate.py
+++ b/src/promptflow-evals/promptflow/evals/evaluate/_evaluate.py
@@ -269,12 +269,13 @@ def evaluate(
     target: Optional[Callable] = None,
     data: Optional[str] = None,
     evaluators: Optional[Dict[str, Callable]] = None,
-    evaluator_config: Optional[Dict[str, Dict[str, str]]] = {},
+    evaluator_config: Optional[Dict[str, Dict[str, str]]] = None,
     azure_ai_project: Optional[Dict] = None,
     output_path: Optional[str] = None,
     **kwargs,
 ):
-    """Evaluates target or data with built-in evaluation metrics
+    """Evaluates target or data with built-in or custom evaluators. If both target and data are provided,
+        data will be run through target function and then results will be evaluated.
 
     :keyword evaluation_name: Display name of the evaluation.
     :paramtype evaluation_name: Optional[str]
@@ -283,14 +284,62 @@ def evaluate(
     :keyword data: Path to the data to be evaluated or passed to target if target is set.
         Only .jsonl format files are supported.  `target` and `data` both cannot be None
     :paramtype data: Optional[str]
-    :keyword evaluator_config: Configuration for evaluators.
+    :keyword evaluators: Evaluators to be used for evaluation. It should be a dictionary with key as alias for evaluator
+        and value as the evaluator function.
+    :paramtype evaluators: Optional[Dict[str, Callable]
+    :keyword evaluator_config: Configuration for evaluators. The configuration should be a dictionary with evaluator
+        names as keys and a dictionary of column mappings as values. The column mappings should be a dictionary with
+        keys as the column names in the evaluator input and values as the column names in the input data or data
+        generated by target.
     :paramtype evaluator_config: Optional[Dict[str, Dict[str, str]]
-    :keyword output_path: The local folder path to save evaluation artifacts to if set
+    :keyword output_path: The local folder or file path to save evaluation results to if set. If folder path is provided
+          the results will be saved to a file named `evaluation_results.json` in the folder.
     :paramtype output_path: Optional[str]
-    :keyword azure_ai_project: Logs evaluation results to AI Studio
+    :keyword azure_ai_project: Logs evaluation results to AI Studio if set.
     :paramtype azure_ai_project: Optional[Dict]
-    :return: A EvaluationResult object.
-    :rtype: ~azure.ai.generative.evaluate.EvaluationResult
+    :return: Evaluation results.
+    :rtype: dict
+
+    :Example:
+
+    Evaluate API can be used as follows:
+
+    .. code-block:: python
+
+            from promptflow.core import AzureOpenAIModelConfiguration
+            from promptflow.evals.evaluate import evaluate
+            from promptflow.evals.evaluators import RelevanceEvaluator, CoherenceEvaluator
+
+
+            model_config = AzureOpenAIModelConfiguration(
+                azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"),
+                api_key=os.environ.get("AZURE_OPENAI_KEY"),
+                azure_deployment=os.environ.get("AZURE_OPENAI_DEPLOYMENT")
+            )
+
+            coherence_eval = CoherenceEvaluator(model_config=model_config)
+            relevance_eval = RelevanceEvaluator(model_config=model_config)
+
+            path = "evaluate_test_data.jsonl"
+            result = evaluate(
+                data=path,
+                evaluators={
+                    "coherence": coherence_eval,
+                    "relevance": relevance_eval,
+                },
+                evaluator_config={
+                    "coherence": {
+                        "answer": "${data.answer}",
+                        "question": "${data.question}"
+                    },
+                    "relevance": {
+                        "answer": "${data.answer}",
+                        "context": "${data.context}",
+                        "question": "${data.question}"
+                    }
+                }
+            )
+
     """
 
     trace_destination = _trace_destination_from_project_scope(azure_ai_project) if azure_ai_project else None
@@ -298,6 +347,8 @@ def evaluate(
     input_data_df = _validate_and_load_data(target, data, evaluators, output_path, azure_ai_project, evaluation_name)
 
     # Process evaluator config to replace ${target.} with ${data.}
+    if evaluator_config is None:
+        evaluator_config = {}
     evaluator_config = _process_evaluator_config(evaluator_config)
     _validate_columns(input_data_df, evaluators, target, evaluator_config)