> For the complete documentation index, see [llms.txt](https://docs.stackai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.stackai.com/agentic-adoption-and-security/observability/evaluator.md).
# Evaluator
The Evaluator View is similar to the batch interface, in that it allows running a CSV file of inputs on your agent, all at once. This view allows testing before a project goes live, and leverages a LLM to evaluate your agent's output.
There are two types of evaluation:
### 1. Grading outputs based on criteria
On the right hand side, create an evaluator:
* Select the output to evaluate
* Add a system prompt - the evaluation logic
* Give it a name
Once the evaluator is created, a new column will appear in the table showing the evaluation results for each row.
Add as many evaluators as outputs in your workflow. Each one will evaluate a different output. Give each evaluator's model a system prompt and select which of your agent's outputs should be evaluated.
You can manually add rows to evaluate, or upload a CSV with all your scenarios to evaluate (click the 3 dots and then the upload CSV option).
### 2. Comparing outputs to a gold standard answer
Click 'Requires Expected Answer' to add a ground truth to your execution. This is the response you would expect from the AI model. The evaluator will then take it into consideration for the analysis.
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