This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of theService Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see thelaunch stage descriptions.

This page describes how to view and interpret your model evaluation results after running your model evaluation using the Gen AI evaluation service.

View evaluation results

The Gen AI evaluation service lets you visualize your evaluation results directly within your development environment, such as a Colab or Jupyter notebook. The.show() method, available on bothEvaluationDataset andEvaluationResult objects, renders an interactive HTML report for analysis.

Visualizing generated rubrics in your dataset

If you runclient.evals.generate_rubrics(), the resultingEvaluationDataset object contains arubric_groups column. You can visualize this dataset to inspect the rubrics generated for each prompt before running the evaluation.

# Example: Generate rubrics using a predefined methoddata_with_rubrics=client.evals.generate_rubrics(src=prompts_df,rubric_group_name="general_quality_rubrics",predefined_spec_name=types.RubricMetric.GENERAL_QUALITY,)# Display the dataset with the generated rubricsdata_with_rubrics.show()

An interactive table displays with each prompt and the associated rubrics generated for it, nested within therubric_groups column:

Visualizing inference results

After generating responses withrun_inference(), you can call.show() on the resultingEvaluationDataset object to inspect the model's outputs alongside your original prompts and references. This is useful for a quick quality check before running a full evaluation:

# First, run inference to get an EvaluationDatasetgpt_response=client.evals.run_inference(model='gpt-4o',src=prompt_df)# Now, visualize the inference resultsgpt_response.show()

A table displays with each prompt, its corresponding reference (if provided), and the newly generated response:

For agent inference, the session inputs (if provided) and intermediate events(if generated) are also displayed.

Visualizing evaluation reports

When you call.show() on anEvaluationResultorEvaluationRun object, areport displays the following sections:

• Summary metrics: An aggregated view of all metrics, showing the mean score and standard deviation across the entire dataset.

• Detailed results: A case-by-case breakdown, allowing you to inspect the prompt, reference, candidate response, and the specific score and explanation for each metric. For agent evaluation, detailed results also include traces showing the agent interactions. For more information on traces, seeTrace an agent.

• Agent information (for agent evaluation only): Information describing the evaluated agent, such as developer instruction, agent description, and tool definitions.

Single candidate evaluation report

For a single model evaluation, the report details the scores for each metric:

# First, run an evaluation on a single candidateeval_result=client.evals.evaluate(dataset=eval_dataset,metrics=[types.RubricMetric.TEXT_QUALITY,types.RubricMetric.FLUENCY,types.Metric(name='rouge_1'),])# Visualize the detailed evaluation reporteval_result.show()

For all reports, you can expand aView Raw JSON section to inspect the data for any structured format such as Gemini, or OpenAI Chat Completion API format.

Adaptive rubric-based evaluation report with verdicts

When using adaptive rubric-based metrics, the results include the pass or fail verdicts and reasoning for each rubric applied to the response.

eval_result=client.evals.evaluate(dataset=eval_dataset,metrics=[types.PrebuiltMetric.GENERAL_QUALITY],)eval_result.show()

The visualization shows each rubric, its verdict (Pass or Fail), and the reasoning, nested within the metric results for each case. For each specific rubric verdict, you can expand a card to show the raw JSON payload. This JSON payload includes additional details such as the full rubric description, the rubric type, importance, and the detailed reasoning behind the verdict.

Multi-candidate comparison report

The report's format adapts depending on whether you are evaluating a single candidate or comparing multiple candidates. For a multi-candidate evaluation, the report provides a side-by-side view and includes win or tie-rate calculations in the summary table.

# Example of comparing two modelsinference_result_1=client.evals.run_inference(model="gemini-2.0-flash",src=prompts_df,)inference_result_2=client.evals.run_inference(model="gemini-2.5-flash",src=prompts_df,)comparison_result=client.evals.evaluate(dataset=[inference_result_1,inference_result_2],metrics=[types.PrebuiltMetric.TEXT_QUALITY])comparison_result.show()