Criteria are a reliable way to detect and correct mistakes in LLM output. Criteria can be used when defining LLM evaluations, improving data quality, and for runtime evaluation when generating best of N samples. This tutorial will walk you through creating and aligning your first criterion.
Before you begin: Before creating your first criterion, you should identify an issue with your model’s output that you want to detect and correct. You should also have either an OpenPipe dataset or a JSONL file containing several rows of data that exhibit the issue, and several that don’t.

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Creating a Criterion

1

Open the creation modal

Navigate to the Criteria tab and click the New Criterion button. The creation modal will open with a default prompt and judge model.
By default, each of the following fields will be templated into the criterion’s prompt when assigning a judgement to an output:
  • messages (optional): The messages used to generate the output
  • tools (optional): The tools used to generate the output
  • tool_choice (optional): The tool choice used to generate the output
  • output (required): The chat completion object to be judged
Many criteria do not require all of the input fields, and some may judge based soley on the output. You can exclude fields by removing them from the Templated Variables section.
2

Draft an initial prompt

Write an initial LLM prompt with basic instructions for identifying rows containing the issue you want to detect and correct. Don’t worry about engineering a perfect prompt, you’ll have a chance to improve it during the alignment process.As an example, if you want to detect rows in which the model’s output is in a different language than the input, you might write a prompt like this:
Mark the criteria as passed if the input and output are the same language.
Mark it as failed if they are in different languages.
Make sure to use the terms input, output, passed, and failed in your prompt to match our internal templating.
Finally, import a few rows (we recommend at least 30) into an alignment set for the criterion.
3

Confirm creation

Click Create to create the criterion and run the initial prompt against the imported alignment set. You’ll be redirected to the criterion’s alignment page.

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Aligning a Criterion

Ensuring your criterion’s judgements are reliable involves two simple processes:
  • Manually labeling outputs
  • Refining the criterion
1

Manually labeling outputs

In order to know whether you agree with your criterion’s judgements, you’ll need to label some data yourself. Use the Alignment UI to manually label each output with PASS or FAIL based on the criterion. Feel free to SKIP outputs you aren’t sure about and come back to them later.
Try to label at least 30 rows to provide a reliable estimate of the LLM’s precision and recall.
2

Refining the criterion

As you record your own judgements, alter the criterion’s prompt and judge model to align its judgements with your own.
Investing time in a good prompt and selecting the best judge model pays dividends. High-quality LLM judgements help you quickly identify rows that fail the criterion, speeding up the process of manually labeling rows.
As you improve your criterion prompt, you’ll notice your alignment stats improving. Once you’ve labeled at least 30 rows and are satisfied with the precision and recall of your LLM judge, the criterion is ready to be deployed!

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Deploying a Criterion

The simplest way to deploy a criterion is to create a criterion eval. Unlike head to head evals, criterion evals are not pairwise comparisons. Instead, they evaluate the quality of one or more models’ output according to a specific criterion. First, navigate to the Evals tab and click New Evaluation -> Add criterion eval. Pick the models to evaluate and the test dataset on which to evaluate them. Next, select the criterion you would like to judge your models against. The judge model and prompt you defined when creating the criterion will be used to judge individual outputs from your models.
Finally, click Create to run the evaluation. Just like that, you’re be able to view evaluation results based on aligned LLM judgements!