Custom Annotation Interfaces for AI Agents

• Collect human judgments on AI outputs
• Label data for training or evaluation
• Correct errors in AI-generated content
• Provide fine-grained feedback beyond simple ratings

@training_annotation
def label_sentiment(text: str) -> str:
    """Human annotators label text as positive/negative/neutral"""
    # Interface presents text with radio button options
    return annotation

@validation_interface
def validate_ai_response(question: str, ai_answer: str) -> tuple[bool, str]:
    """Annotators verify correctness and provide corrections"""
    # Interface shows question-answer pair with correction field
    return (is_correct, corrected_answer)

@error_annotation
def categorize_error(ai_output: str, reference: str) -> str:
    """Annotators identify error types (factual, coherence, etc.)"""
    # Dropdown with error taxonomy appears next to each output
    return error_type

• Create React/Angular/Vue interfaces connected to your backend
• Use specialized frameworks like Prodigy, Label Studio, or Brat

from flask import Flask, request

app = Flask(__name__)

@app.route('/annotate', methods=['POST'])
def annotation_endpoint():
    data = request.json
    # Store annotations in your database
    return {"status": "success"}

from ipywidgets import interact

@interact
def annotate(text="AI generated text", sentiment=['positive', 'negative', 'neutral']):
    save_annotation(text, sentiment)

1. Design for efficiency: Minimize clicks and typing
2. Provide clear guidelines: Include annotation instructions
3. Ensure quality control: Implement reviewer agreement metrics
4. Iterate on design: Improve based on annotator feedback
5. Track provenance: Record who provided which annotations

def train_with_annotations():
    raw_data = load_dataset()
    annotations = load_human_annotations()
    
    # Convert to training format
    training_data = [(d['input'], a['label']) for d, a in zip(raw_data, annotations)]
    
    # Fine-tune your model
    model.train(training_data)