In a real , store task in a database [chapter] deterministic

return {"task_id": str(uuid.uuid4()), "data_id": task.data_id, "label": task.label}

`

This snippet demonstrates how to protect the annotation endpoint with JWT tokens and how to record a simple label. In production, you would replace the in-memory dictionary with a proper database, add pagination, and integrate with a UI framework such as Streamlit or React.

Preference Learning and RLHF Reinforcement Learning from Human Feedback (RLHF) is a technique that aligns language models with human preferences by training a reward model on annotated preference pairs. The typical RLHF pipeline consists of three stages: 1. **Data collection:** Annotators rank pairs of model outputs or provide pairwise preferences. 2. **Reward model training:** A model learns to predict which output a human prefers. 3. **Policy fine-tuning:** The base language model is fine-tuned using the reward model as a guide (often via Proximal Policy Optimization, PPO, or Direct Preference Optimization, DPO). In a local-first setting, you can keep all data on-premises, preserving privacy while still leveraging human feedback. To make the annotation process more structured, you can use a **CLASSIFIER_SYSTEM_PROMPT** to categorize each feedback example into predefined classes such as “helpful”, “harmful”, or “neutral”. This classification can feed into downstream quality checks and help you maintain a balanced dataset. The **CRITIQUE_SYSTEM_PROMPT** can be employed to evaluate the quality of generated responses before they are submitted to human annotators. By automatically flagging low-quality outputs, you reduce annotator workload and improve the signal-to-noise ratio of the feedback. Similarly, the **SYNTHESIS_SYSTEM_PROMPT** can be used to combine multiple annotations into a consensus label, which is especially useful when multiple annotators provide conflicting opinions.

Implementing RLHF Locally Below is a simplified example of how to set up a local RLHF pipeline using the Hugging Face Transformers library. This code assumes you have already collected preference pairs and stored them in a pandas DataFrame called preferences.

```python