# Evaluation

### Overview

In Datasaur, we support three types of evaluation. You can choose the one that best suits your needs.

* [Automated evaluation](https://docs.datasaur.ai/llm-projects/evaluation/automated-evaluation)
* [Ranking](https://docs.datasaur.ai/llm-projects/evaluation/ranking-rlhf)
* [Rating](https://docs.datasaur.ai/llm-projects/evaluation/rating)

### Evaluations

#### Automated evaluation

You can evaluate an LLM application or pre-generated completions using your preferred metrics by comparing them to a ground truth. Currently, we support **Answer correctness** metric from LangChain and Ragas. More metrics will be available soon!

#### Ranking

Evaluate your model by ranking several completion results from each prompt from best to worst. You can evaluate pre-generated completions or an LLM application and determine how many results it needs to generate.

#### Rating

Evaluate each completion result from each prompt by rating them with 1 to 5 stars and providing your expected completions. You can evaluate pre-generated completions or an LLM application.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.datasaur.ai/llm-projects/evaluation.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.