Evaluations (often called evals) test model outputs to ensure they meet your specified style and content criteria. Writing evals is an essential part of building reliable applications. Datasets, a feature of the OpenAI platform, provide a quick way to get started with evals and test prompts.

First, create a dataset in the dashboard.

1. On the evaluation page, navigate to the Datasets tab.
2. Click the Create button in the top right to get started.
3. Add a name for your dataset in the input field. In this guide, we’ll name our dataset “Investment memo generation.”
4. Add data. To build your dataset from scratch, click Create and start adding data through our visual interface. If you already have a saved prompt or a CSV with data, upload it.

We recommend using your dataset as a dynamic space, expanding your set of evaluation data over time. As you identify edge cases or blind spots that need monitoring, add them using the dashboard interface.

Uploading a CSV

We have a simple CSV containing company names and actual values for their revenue from past quarters.

The columns in your CSV are accessible to both your prompt and graders. For example, our CSV contains input columns (company) and ground truth columns (correct_revenue, correct_income) for our graders to use as reference.

Using the visual data interface

After opening your dataset, you can manipulate your data in the Data tab. Click a cell to edit its contents. Add a row to add more data. You can also delete or duplicate rows in the overflow menu at the right edge of each row.

To save your changes, click Save button in the top right.

The tabs in the datasets dashboard let multiple prompts interact with the same data.

1. To add a new prompt, click Add prompt.

   Datasets are designed to be used with your OpenAI prompts. If you’ve saved a prompt on the OpenAI platform, you’ll be able to select it from the dropdown and make changes in this interface. To save your prompt changes, click Save.

   Our prompts use a versioning system so you can safely make updates. Clicking Save creates a new version of your prompt, which you can refer to or use anywhere in the OpenAI platform.

2. In the prompt panel, use the provided fields and settings to control the inference call:

• Click the slider icon in the top right to control model temperature and top_p.
• Add tools to grant your inference call the ability to access the web, use an MCP, or complete other tool-call actions.
• Add variables. The prompt and your graders can both refer to these variables.
• Type your system message directly, or click the pencil icon to have a model help generate a prompt for you, based on basic instructions you provide.

In our example, we’ll add the web search tool so our model call can pull financial data from the internet. In our variables list, we’ll add company so our prompt can reference the company column in our dataset. And for the prompt, we’ll generate one by telling the model to “generate a financial report.”

With your data and prompt set up, you’re ready to generate outputs. The model’s output gives you a sense of how the model performs your task with the prompt and tools you provided. You’ll then annotate the outputs so the model can improve its performance over time.

1. In the top right, click Generate output.

   You’ll see a new special output column in the dataset begin to populate with results. This column contains the results from running your prompt on each row in your dataset.

2. Once your generated outputs are ready, annotate them. Open the annotation view by clicking the output, rating, or output_feedback column.

   Annotate as little or as much as you want. Datasets are designed to work with any degree and type of annotation, but the higher quality of information you can provide, the better your results will be.

What annotation does

Annotations are a key part of evaluating and improving model output. A good annotation:

• Serves as ground truth for desired model behavior, even for highly specific cases—including subjective elements, like style and tone
• Provides information-dense context enabling automatic prompt improvement (via our prompt optimizer)
• Enables diagnosing prompt shortcomings, particularly in subtle or infrequent cases
• Helps ensure that graders are aligned with your intent

You can choose to annotate as little or as much as you want. Datasets are designed to work with any degree and type of annotation, but the higher quality of information you can provide, the better your results will be. Additionally, if you’re not an expert on the contents of your dataset, we recommend that a subject matter expert performs the annotation — this is the most valuable way for their expertise to be incorporated into your optimization process. Explore our cookbook to learn more about what we have found to be most effective in using evals to improve our prompt resilience.

Annotation starting points

Here are a few types of annotations you can use to get started:

• A Good/Bad rating, indicating your judgment of the output
• A text critique in the output_feedback section
• Custom annotation categories that you added in the Columns dropdown in the top right

Incorporate expert annotations

If you’re not an expert on the contents of your dataset, have a subject matter expert perform the annotation. This is the best way to incorporate expertise into the optimization process. Explore our cookbook to learn more.

While annotations are the most effective way to incorporate human feedback into your evaluation process, graders let you run evaluations at scale. Graders are automated assessments that can produce a variety of inputs depending on their type.

1. In the top right, navigate to Grade > New grader.
2. From the dropdown, choose your grader type, and fill out the form to compose your grader.
3. Reference the columns from your dataset to check against ground truth values.
4. Create the grader.
5. Once you’ve added at least one grader, use the Grade dropdown menu to run specific graders or all graders on your dataset. When a run is complete, you’ll see pass/fail ratings in your dataset in a dedicated column for each grader.

After saving your dataset, graders persist as you make changes to your dataset and prompt, making them a great way to quickly assess whether a prompt or model parameter change leads to improvements, or whether adding edge cases reveals shortcomings in your prompt. The datasets dashboard supports multiple tabs for simultaneously tracking results from automated graders across multiple variants of a prompt.

Datasets are great for rapid iteration. When you’re ready to track performance over time or run at scale, export your dataset to an Eval. Evals run asynchronously, support larger data volumes, and let you monitor performance across versions.

For more inspiration, visit the OpenAI Cookbook, which contains example code and links to third-party resources, or learn more about our evaluation tools: