> For the complete documentation index, see [llms.txt](https://docs.eyepop.ai/developer-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.eyepop.ai/developer-documentation/train-your-own-model/using-eyepop.ais-autolabeler.md).

# Using EyePop.ai’s AutoLabeler

Test a prompt, then let the AutoLabeler pre-label your dataset

As your data uploads, EyePop.ai’s AutoLabeller begins identifying and prioritizing images which are most dissimilar from the dataset for human review. This simplifies and accelerates labeling by limiting the number of images that require your review.

Once data is uploaded, EyePop.ai directs you into **prompt testing**. Your uploaded images appear in a paginated grid on the right hand side of the screen; page through them to find good, representative examples of things you might expect in operation. Then test out different prompts on the left side of the screen to ensure your model understands your object. For this example, we might experiment with terms like “glasses” or “eyewear.” The model will place bounding boxes where it thinks it sees your object prompt.

If your model adds too many or not enough object identification boxes within an image, refine the prompt: name the object more precisely, or say what to exclude. Once the model is trained, you can also tune a `confidenceThreshold` on the inference component when you run it — see [Components](/developer-documentation/platform/pop/pop-components.md).

When you feel good about your prompt, click **I’m ready to auto label**. This step may take a while. The first time around your model is using EyePop.ai’s generic auto-labeler. Each time you work through the human review process and iterate with new data, your own pre-trained model will be used for this step, so it will improve each time you work through the dataset.