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

# Functional Evaluation

Functional evaluation is a critical aspect of ensuring your AI applications perform as expected and deliver accurate, reliable, and safe outputs. Trusys provides a robust Prompt Library feature that allows you to systematically test and evaluate your AI models using a variety of prompts and predefined metrics.

> **Key Steps**
>
> * Connect your AI application or LLM model
> * Create a prompt library with datasets and variables
> * Run functional evaluation using the prompt library

Trusys enables you to evaluate your AI applications with structured **prompt libraries**, **datasets**, and **functional evaluations**. This helps you assess accuracy, reliability, and safety before deployment.
A **Functional Evaluation** runs prompts or datasets against your connected AI applications or models, scoring responses based on defined metrics.

***

# Run a New Evaluation

To initiate a new evaluation, navigate to the ‘Test Run’ section and follow these steps:

<Steps>
  <Step title="Select an Application">
    `Choose the AI application` or LLM model you wish to evaluate from your list of connected applications. This is the target for your test run.
  </Step>

  <Step title="Select a Prompt Library">
    `Select the Prompt Library` that contains the prompts you want to use for this evaluation. The prompts within this library will serve as the inputs for your chosen application.
  </Step>

  <Step title="Run Evaluation">
    Review the selected application(s) and prompt libraries. Once confirmed, click **Run Evaluation** to initiate the test run. Trusys will then execute the prompts, collect responses, and evaluate against the defined metrics.
  </Step>
</Steps>

Trusys executes prompts, collects responses, and evaluates them against metrics.

***

# Evaluation Run List

The list view shows:

* **Status** (Pending, Running, Completed, Failed)
* **Application** evaluated
* **Prompt Library** used
* **Start Time** of the run
* **Total Prompts** executed
* **Passed/Failed Metrics Count**

This list helps you monitor the progress of ongoing evaluations and quickly identify completed or failed test runs.

***

# Evaluation Run Details

Click a run to see detailed results:

* **Overall Summary** – Pass/fail rate, metric performance
* **Prompt-by-Prompt Analysis**
  – Input Prompt: The exact prompt that was sent to the AI application.
  * AI Response: The response received from your AI application.
  * Metric Results: The individual scores for each metric applied to that specific prompt-response pair, along with whether it passed or failed its expected value.
  * Variable Values: If variables were used, the specific values that were substituted for that test case.
* **Metric Report** – Average scores, distributions, pass/fail counts
* **Comparison View (optional)** – Compare multiple runs across applications

<Tip>
  Use both prompt libraries (for targeted tests) and datasets (for comprehensive benchmarking) to get the most reliable evaluation results.
</Tip>