# LLM and Vision Pre-Annotation

Source: https://www.potatoannotator.com/docs/guides/llm-pre-annotation

**Pre-annotation uses a model to propose labels that a human then verifies or corrects. Checking a good suggestion is far faster than labeling from scratch, so pre-annotation can cut annotation time substantially, as long as you keep a human in the loop.** This is [human-in-the-loop](https://en.wikipedia.org/wiki/Human-in-the-loop) machine learning.

Potato has built-in AI support for [OpenAI, Claude, Gemini, Ollama, and others](/docs/features/ai-support).

## How pre-annotation works

1. A model (an LLM, or a vision model for images) predicts a label for each item.
2. The prediction is shown to the annotator as a pre-filled suggestion or a highlighted option.
3. The annotator confirms or fixes it.
4. The verified label, not the raw model output, becomes your data.

## Turning it on

```yaml
ai_support:
  enabled: true
  endpoint_type: openai      # or anthropic, gemini, ollama, ...
  ai_config:
    model: gpt-4
    api_key: ${OPENAI_API_KEY}
    temperature: 0.3
```

Potato offers a few flavors:

- **[In-context learning labeling](/docs/features/icl-labeling)**: the model labels items from a few examples in the prompt; the human verifies.
- **[Option highlighting](/docs/features/option-highlighting)**: the model pre-selects the labels it thinks are most likely, so the annotator confirms rather than searches.
- **[Visual AI support](/docs/features/visual-ai-support)**: vision models (GPT-4V, Claude, Gemini, or a detector like YOLO) propose image labels and boxes.

## The risk: automation bias

The danger of pre-annotation is [automation bias](https://en.wikipedia.org/wiki/Automation_bias), annotators rubber-stamp the model's suggestions, importing its errors into your "gold" data. Guard against it:

- Keep [gold standards](/docs/guides/gold-standards-and-attention-checks) running so you can detect blind acceptance.
- Don't pre-fill on the items you use to measure [agreement](/docs/guides/inter-annotator-agreement); measure on un-suggested items.
- Use lower-confidence suggestions as *hints*, not defaults, for hard cases.

## Pre-annotation vs. active learning

Pre-annotation makes each label faster. [Active learning](/docs/guides/active-learning) makes each label more *valuable* by choosing which items to label next. They combine well.

## Further reading

- [AI Support feature reference](/docs/features/ai-support)
- [Active Learning for Annotation](/docs/guides/active-learning)
- [Solo Mode](/docs/features/solo-mode), a guided human-plus-LLM workflow