> ## Documentation Index
> Fetch the complete documentation index at: https://docs.asserto.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Testing & Evaluation Concepts

> Core concepts for designing automated test cases and evaluations for LLM prompts in Asserto AI.

## Introduction

In Asserto AI, Testing and Evaluations are organized with Test Cases and Assertions.
Assertions are checks on the output that match your expectations.

Tests are built as data-driven, no code required. You define input data and expected outputs, and the system checks whether the prompt output matches your expectations.

<Note>
  The term **Automated tests** refers to the automated validation and scoring of the performed tests and evaluations. No human is needed to validate the outputs. Tests are not created automatically — this system helps you create and run tests.
</Note>

## Test Cases

A test case defines:

**Input data**: Set the value for individual parameters used in your prompt templates.

**Assertions**: A test case can define one or more assertions.

## Assertions

Supported operations:

* equal
* contains
* greater

Often the LLM output does not provide a direct value to be checked on. In this case a computation can be used as an intermediate step in the assertion.

## Computations

Computations can be code-based or LLM-based (aka LLM-as-Judge).

Both reference-free and reference-based computations are supported.
In case of reference-based computation, you need to input a target value (or ground truth).

### Code-based computations

<ResponseField name="character-count" type="reference-free">
  A simple character-count to measure the output length.
</ResponseField>

<ResponseField name="similarity" type="reference-based">
  Semantic Textual Similarity based on `cross-encoder/stsb-distilroberta-base`.

  To be used when you need accurate similarity scores for sentence pairs.

  * "My app crashes when clicking login"
  * "App crashes after pressing login button"
</ResponseField>