> ## Documentation Index
> Fetch the complete documentation index at: https://promptlayer-1023-gorgias-docs.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Getting Started

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The overall process of building an evaluation pipeline looks like this:

1. **Select Your Dataset**: Choose or upload datasets to serve as the basis for your evaluations, whether for scoring, regression testing, or bulk job processing.
2. **Build Your Pipeline**: Start by visually constructing your evaluation pipeline, defining each step from input data processing to final evaluation.
3. **Run Evaluations**: Execute your pipeline, observe the results in a spreadsheet-like interface, and make informed decisions based on comprehensive metrics and scores.

## Creating a Pipeline

1. **Initiate a Batch Run**: Start by creating a new batch run, which requires specifying a name and selecting a dataset.
2. **Dataset Selection**: Upload a CSV/JSON dataset, or create a dataset from historical data using filters like time range, prompt template logs, scores, and metadata. [Learn more here.](/features/evaluations/datasets)

You now have a pipeline. Preview mode allows you to iterate with live feedback, allowing for adjustments in real-time.

## Setting up the Pipeline

### Adding Steps

Click 'Add Step' to start building your pipeline, with each column representing a step in the evaluation process.

Steps execute in order left to right. That means that if a column depends on a previous column, make sure it appears to the right of the dependency.

#### Common Step Types

* **Prompt Template**: Select a prompt template from the registry, set model parameters, LLM, arguments, and template version.
* **Custom API Endpoint**: Define a URL to send and receive data, suitable for custom evaluators or external systems.
* **Human Input**: Engage human graders by adding a step that allows for textual input.
* **String Comparison**: Use this step to compare the outputs of two previous step, showing a visual diff when relevant.

#### Scoring

If the last step of your evaluation pipeline contains all booleans or numeric values, that will be consider the score for the row. Your full evaluation report will have a scorecard of the average of this last step.

*NOTE: All cells in the last column must be boolean or all must be numeric. If any cell deviates, the score will not be calculated*

## Executing Full Batch Runs

Transition from pipeline to full batch run to apply your pipeline across the entire dataset for comprehensive evaluation.