# Collection Methods

* **Rating outputs**: People rate AI-generated content (good/bad, helpful/harmful, preferred/less preferred).
* **Pairwise comparisons**: Given two outputs, humans pick which is better.
* **Direct edits or suggestions**: Annotators or users improve the AI’s output (e.g., rewriting text or correcting errors).
* **Specialized feedback**: Domain experts (e.g., lawyers, doctors, teachers) review content for accuracy in specialized fields.
* Feedback is turned into training signals for techniques like **Reinforcement Learning from Human Feedback (RLHF)** or **Direct Preference Optimization (DPO)**.

<figure><img src="https://2099145561-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F4K2TU7ZCXEZ3MXHzOmmd%2Fuploads%2FStluFmgZlKyWL3OWmvRJ%2FScreenshot%202025-10-07%20at%2010.04.19%E2%80%AFPM.png?alt=media&#x26;token=9180194f-1243-42d6-a96b-9731098d071a" alt=""><figcaption></figcaption></figure>
