Submit batch run and evaluate a flow

To evaluate how well your flow performs with a large dataset, you can submit batch run and use built-in evaluation methods in prompt flow.

In this article you'll learn to:

  • Submit a Batch Run and Use a Built-in Evaluation Method
  • View the evaluation result and metrics
  • Start A New Round of Evaluation
  • Check Batch Run History and Compare Metrics
  • Understand the Built-in Evaluation Metrics
  • Ways to Improve Flow Performance
  • Further reading: Guidance for creating Golden Datasets used for Copilot quality assurance

You can quickly start testing and evaluating your flow by following this video tutorial submit batch run and evaluate a flow video tutorial.

Prerequisites

To run a batch run and use an evaluation method, you need to have the following ready:

  • A test dataset for batch run. Your dataset should be in one of these formats: .csv, .tsv, or .jsonl. Your data should also include headers that match the input names of your flow. Further Reading: If you are building your own copilot, we recommend referring to Guidance for creating Golden Datasets used for Copilot quality assurance.
  • An available runtime to run your batch run. A runtime is a cloud-based resource that executes your flow and generates outputs. To learn more about runtime, see Runtime.

Submit a batch run and use a built-in evaluation method

A batch run allows you to run your flow with a large dataset and generate outputs for each data row. You can also choose an evaluation method to compare the output of your flow with certain criteria and goals. An evaluation method is a special type of flow that calculates metrics for your flow output based on different aspects. An evaluation run will be executed to calculate the metrics when submitted with the batch run.

To start a batch run with evaluation, you can select on the "Evaluate" button on the top right corner of your flow page.

Screenshot of Web Classification with batch run highlighted.

To submit batch run, you can select a dataset to test your flow with. You can also select an evaluation method to calculate metrics for your flow output. If you don't want to use an evaluation method, you can skip this step and run the batch run without calculating any metrics. You can also start a new round of evaluation later.

First, you're asked to give your batch run a descriptive and recognizable name. You can also write a description and add tags (key-value pairs) to your batch run. After you finish the configuration, select "Next" to continue.

Screenshot of batch run settings where you specify run name and description.

Second, you need to select or upload a dataset that you want to test your flow with. You also need to select an available runtime to execute this batch run. Prompt flow also supports mapping your flow input to a specific data column in your dataset. This means that you can assign a column to a certain input. You can assign a column to an input by referencing with ${data.XXX} format. If you want to assign a constant value to an input, you can directly type in that value.

Screenshot of batch run settings where you select a test dataset.

Then, in the next step, you can decide to use an evaluation method to validate the performance of this run either immediately or later. For a completed batch run, a new round of evaluation can still be added.

You can directly select the "Next" button to skip this step and run the batch run without using any evaluation method to calculate metrics. In this way, this batch run only generates outputs for your dataset. You can check the outputs manually or export them for further analysis with other methods.

Otherwise, if you want to run batch run with evaluation now, you can select one or more evaluation methods based on the description provided. You can select "More detail" button to see more information about the evaluation method, such as the metrics it generates and the connections and inputs it requires.

Screenshot of evaluation settings where you can select built-in evaluation method.

Go to the next step and configure evaluation settings. In the "Evaluation input mapping" section, you need to specify the sources of the input data that are needed for the evaluation method. For example, ground truth column might come from a dataset. By default, evaluation will use the same dataset as the test dataset provided to the tested run. However, if the corresponding labels or target ground truth values are in a different dataset, you can easily switch to that one.

Therefore, to run an evaluation, you need to indicate the sources of these required inputs. To do so, when submitting an evaluation, you'll see an "Evaluation input mapping" section.

  • If the data source is from your run output, the source is indicated as "${run.output.[OutputName]}"
  • If the data source is from your test dataset, the source is indicated as "${data.[ColumnName]}"

Screenshot of evaluation input mapping.

Note

If your evaluation doesn't require data from the dataset, you do not need to reference any dataset columns in the input mapping section, indicating the dataset selection is an optional configuration. Dataset selection won't affect evaluation result.

If an evaluation method uses Large Language Models (LLMs) to measure the performance of the flow response, you're also required to set connections for the LLM nodes in the evaluation methods.

Screenshot of connection where you can configure the connection for evaluation method.

Note

Some evaluation methods require GPT-4 or GPT-3 to run. You must provide valid connections for these evaluation methods before using them.

After you finish the input mapping, select on "Next" to review your settings and select on "Submit" to start the batch run with evaluation.

View the evaluation result and metrics

After submission, you can find the submitted batch run in the run list tab in prompt flow page. Select a run to navigate to the run detail page.

Screenshot of prompt flow run list page where you find batch runs.

In the run detail page, you can select Details to check the details of this batch run.

Screenshot of batch run detail page where you view detailed information.

In the details panel, you can check the metadata of this run. You can also go to the Outputs tab in the batch run detail page to check the outputs/responses generated by the flow with the dataset that you provided. You can also select "Export" to export and download the outputs in a .csv file.

Screenshot of batch run detail page on the outputs tab where you check batch run outputs.

You can select an evaluation run from the dropdown box and you'll see appended columns at the end of the table showing the evaluation result for each row of data. You can locate the result that is falsely predicted with the output column "grade".

Screenshot of batch run detail page on the outputs tab where evaluation results are appended.

To view the overall performance, you can select the Metrics tab, and you can see various metrics that indicate the quality of each variant.

Screenshot of batch run detail page on the metrics tab where you check the overall performance in the metrics tab.

To learn more about the metrics calculated by the built-in evaluation methods, navigate to understand the built-in evaluation metrics.

Start a new round of evaluation

If you have already completed a batch run, you can start another round of evaluation to submit a new evaluation run to calculate metrics for the outputs without running your flow again. This is helpful and can save your cost to rerun your flow when:

  • you didn't select an evaluation method to calculate the metrics when submitting the batch run, and decide to do it now.
  • you have already used evaluation method to calculate a metric. You can start another round of evaluation to calculate another metric.
  • your evaluation run failed but your flow successfully generated outputs. You can submit your evaluation again.

You can select Evaluate to start another round of evaluation.

Screenshot of batch run detail page on where to start a new round of evaluation.

After setting up the configuration, you can select "Submit" for this new round of evaluation. After submission, you'll be able to see a new record in the prompt flow run list.

After the evaluation run completed, similarly, you can check the result of evaluation in the "Outputs" tab of the batch run detail panel. You need select the new evaluation run to view its result.

Screenshot of batch run detail page on the output tab with checking the new evaluation output.

When multiple different evaluation runs are submitted for a batch run, you can go to the "Metrics" tab of the batch run detail page to compare all the metrics.

Check batch run history and compare metrics

In some scenarios, you'll modify your flow to improve its performance. You can submit multiple batch runs to compare the performance of your flow with different versions. You can also compare the metrics calculated by different evaluation methods to see which one is more suitable for your flow.

To check the batch run history of your flow, you can select the "View batch run" button on the top right corner of your flow page. You'll see a list of batch runs that you have submitted for this flow.

Screenshot of Web Classification with the view bulk runs button selected.

You can select on each batch run to check the detail. You can also select multiple batch runs and select on the "Visualize outputs" to compare the metrics and the outputs of these batch runs.

Screenshot of batch run runs showing the history.

In the "Visualize output" panel the Runs & metrics table shows the information of the selected runs with highlight. Other runs that take the outputs of the selected runs as input are also listed.

In the "Outputs" table, you can compare the selected batch runs by each line of sample. By selecting the "eye visualizing" icon in the "Runs & metrics" table, outputs of that run will be appended to the corresponding base run.

Screenshot of metrics compare of multiple batch runs.

Understand the built-in evaluation metrics

In prompt flow, we provide multiple built-in evaluation methods to help you measure the performance of your flow output. Each evaluation method calculates different metrics. Now we provide nine built-in evaluation methods available, you can check the following table for a quick reference:

Evaluation Method Metrics Description Connection Required Required Input Score Value
Classification Accuracy Evaluation Accuracy Measures the performance of a classification system by comparing its outputs to ground truth. No prediction, ground truth in the range [0, 1].
QnA Relevance Scores Pairwise Evaluation Score, win/lose Assesses the quality of answers generated by a question answering system. It involves assigning relevance scores to each answer based on how well it matches the user question, comparing different answers to a baseline answer, and aggregating the results to produce metrics such as averaged win rates and relevance scores. Yes question, answer (no ground truth or context) Score: 0-100, win/lose: 1/0
QnA Groundedness Evaluation Groundedness Measures how grounded the model's predicted answers are in the input source. Even if LLM’s responses are true, if not verifiable against source, then is ungrounded. Yes question, answer, context (no ground truth) 1 to 5, with 1 being the worst and 5 being the best.
QnA GPT Similarity Evaluation GPT Similarity Measures similarity between user-provided ground truth answers and the model predicted answer using GPT Model. Yes question, answer, ground truth (context not needed) 1 to 5, with 1 being the worst and 5 being the best.
QnA Relevance Evaluation Relevance Measures how relevant the model's predicted answers are to the questions asked. Yes question, answer, context (no ground truth) 1 to 5, with 1 being the worst and 5 being the best.
QnA Coherence Evaluation Coherence Measures the quality of all sentences in a model's predicted answer and how they fit together naturally. Yes question, answer (no ground truth or context) 1 to 5, with 1 being the worst and 5 being the best.
QnA Fluency Evaluation Fluency Measures how grammatically and linguistically correct the model's predicted answer is. Yes question, answer (no ground truth or context) 1 to 5, with 1 being the worst and 5 being the best
QnA f1 scores Evaluation F1 score Measures the ratio of the number of shared words between the model prediction and the ground truth. No question, answer, ground truth (context not needed) in the range [0, 1].
QnA Ada Similarity Evaluation Ada Similarity Computes sentence (document) level embeddings using Ada embeddings API for both ground truth and prediction. Then computes cosine similarity between them (one floating point number) Yes question, answer, ground truth (context not needed) in the range [0, 1].

Ways to improve flow performance

After checking the built-in metrics from the evaluation, you can try to improve your flow performance by:

  • Check the output data to debug any potential failure of your flow.
  • Modify your flow to improve its performance. This includes but not limited to:
    • Modify the prompt
    • Modify the system message
    • Modify parameters of the flow
    • Modify the flow logic

Prompt construction can be difficult. We provide a Introduction to prompt engineering to help you learn about the concept of constructing a prompt that can achieve your goal. See prompt engineering techniques to learn more about how to construct a prompt that can achieve your goal.

System message, sometimes referred to as a metaprompt or system prompt that can be used to guide an AI system’s behavior and improve system performance. Read this document on System message framework and template recommendations for Large Language Models(LLMs) to learn about how to improve your flow performance with system message.

Further reading: Guidance for creating Golden Datasets used for Copilot quality assurance

The creation of copilot that use Large Language Models (LLMs) typically involves grounding the model in reality using source datasets. However, to ensure that the LLMs provide the most accurate and useful responses to customer queries, a "Golden Dataset" is necessary.

A Golden Dataset is a collection of realistic customer questions and expertly crafted answers. It serves as a Quality Assurance tool for LLMs used by your copilot. Golden Datasets are not used to train an LLM or inject context into an LLM prompt. Instead, they are utilized to assess the quality of the answers generated by the LLM.

If your scenario involves a copilot or if you are in the process of building your own copilot, we recommend referring to this specific document: Producing Golden Datasets: Guidance for creating Golden Datasets used for Copilot quality assurance for more detailed guidance and best practices.

Next steps

In this document, you learned how to submit a batch run and use a built-in evaluation method to measure the quality of your flow output. You also learned how to view the evaluation result and metrics, and how to start a new round of evaluation with a different method or subset of variants. We hope this document helps you improve your flow performance and achieve your goals with Prompt flow.