No code evals

Before you start, pick a dataset to evaluate. You can run no-code eval for a separate dataset you have or over data that’s already on the platform.

You can run the following type of evals:

Name	Type	Description
Text Evals	Configurable Preset	Analyze text data, from regular expressions to LLM judges.
Data Quality	Fixed Preset	Get descriptive statistics for all columns and the dataset.
Classification Quality	Fixed Preset	Evaluate the quality on a classification task.
Regression Quality	Fixed Preset	Evaluate the quality on a regression task.

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1. Create or Select a Project

If you do not have an existing Project, create one.

On the main page of Evidently Platform, click “Create new Project.” Give your Project a title and description to help organize your evaluations.

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2. Start an evaluation

Inside the Project, go to the “Evaluate” menu item on the left. Let’s start!

(Optional) Add Open AI key. Add a token in the “Secrets” menu section if you plan to use an LLM for evaluations. You can proceed without it, using other types of evals.

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3. Pass the Dataset

There are two options:

• Upload a CSV. Drag and drop your Dataset.

• Use an existing Dataset. Select a Dataset you previously uploaded to the platform or one collected through Tracing.

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4. Data Definition

Your dataset can have any structure, but certain evaluations require specific columns:

• Text evaluations: make sure there’s at least one text column.

• Regression and classification: include columns with predictions and ground truth.

• Data quality: any dataset with categorical, numerical or text columns.

To proceed, map your data schema. You don’t have to fill in all the options: choose what applies. For example, for text evals, point to the columns containing text.

If you have a timestamp, map it as a “DateTime.” You will get extra plots with score changes in time.

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5. Configure the evaluation

Some evaluations (Classification, Regression, and Data Quality) run as is. Once you passed the data, hit “Run” to get your Report.

Text Evals require a bit more setup. You must choose which column to evaluate and how. You can choose from the following methods:

• Model-based: use built-in machine learning models, like sentiment analysis.

• Regular expressions: check for specific words or patterns.

• Text stats: measure stats like the number of symbols or sentences.

• LLM-based: use external LLMs to evaluate your text data.

Select specific checks one by one:

Each evaluation result is called a Descriptor. No matter the method, you’ll get a label or score for every evaluated text. Some, like “Sentiment,” work instantly, while others may need setup.

Here are few examples of Descriptors and how to configure them:

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Words presence

Include Words. This Descriptor checks for listed words and returns “True” or “False.”

Set up these parameters:

• Add a list of words.

• Choose whether to check for “any” or “all” of the words present.

• Set the lemmatize parameter to check for inflected and variant words automatically.

• Give your check a name so you can easily find it in your results.

Example setup:

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Semantic Similarity

Semantic Similarity. This descriptor converts texts to embeddings and calculates Cosine Similarity between your evaluated column and another column. It scores from 0 to 1 (0: completely different, 0.5: unrelated, 1: identical). It’s useful for checking if responses are semantically similar to a question or reference.

Select the column to compare against:

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LLM as a judge

Custom LLM evaluator. If you’ve added your token, use LLM-based evals (built-in or custom) to send your texts to LLMs for grading or scoring. You can choose a specific LLM model from the provider.

For example, you can create a custom evaluator to classify texts as “cheerful” or “neutral.” Fill in the parameters, and Evidently will generate the evaluation prompt:

For a binary classification template, you can configure:

• Criteria: define custom criteria in free text to clarify the classification task.

• Target/Non-target Category: labels you want to use.

• Uncertain Category: how the model should respond when it can’t decide.

• Reasoning: choose to include explanation (Recommended).

• Category and/or Score: have the LLM respond with the category (Recommended) or score.

• Visualize as: when both Category and Score are computed, choose which to display in the Report.

To add evaluations for another column in the same Report, click “Add Preset,” select “Text Evals,” and follow the same steps for the new column. You can include evals for multiple columns at once.

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6. Run the evaluation

Before generating the Report, you can add a Tag to identify or reference the Report later. It’s optional.

Click “Run”, and the calculation will start! It may take some time to process, especially on a large dataset.

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7. View the results

Once your evaluation is complete, you can view the results. You can either

• view the Dataset with the added scores, or

• use the Explore view to see the Dataset with a summary Report (recommended).

If you have many Metrics, the Explore page will initially show the long Report. Use the selector at the top to focus on a specific Metric.

Here’s what the Explore page might look like with the results from the Cheerfulness evaluator for the “Answer” column: