Efficient Workflow for Semi-Automatic Data Labeling Using LLM-based Zero-Shot Prompting

Stop manually labeling thousands of rows of text when you can treat a high-end LLM as a "silver-standard" annotator to bootstrap your dataset. I’ve been using Cursor’s Composer mode combined with a Python script to handle semi-automatic labeling for a sentiment analysis project, and the productivity jump is massive compared to traditional manual tagging.

The core strategy is to use zero-shot prompting to generate initial labels, then use a confidence-based filtering system to isolate the samples where the AI is uncertain. This prevents you from blindly trusting the LLM while still removing 80% of the manual drudgery.

Here is the setup I use. I keep a .cursorrules file in my project root that tells Cursor to always prefer Pydantic for data validation, which ensures the LLM outputs structured JSON instead of conversational fluff.

I start by writing a labeling script that sends batches of data to Claude 3.5 Sonnet. The key is to force the model to provide a "confidence score" (0-1) for every label it assigns.

import json
from pydantic import BaseModel
from typing import List

class LabelResult(BaseModel):
    label: str
    confidence: float
    reasoning: str

def get_llm_label(text, categories):
    prompt = f"""
    Categorize the following text into one of these categories: {categories}.
    Text: {text}
    Return only valid JSON matching the schema: label, confidence, reasoning.
    """
    # Implementation using your preferred LLM SDK
    # response = client.chat.completions.create(...)
    # return response.json()

Once the script runs, I don't just accept the results. I apply a threshold—usually 0.85. Anything above that is marked as "Silver Label." Anything below is flagged for "Human Review."

To speed up the review process, I use Cursor to build a quick Streamlit internal tool. Instead of scrolling through a CSV, I have a UI that shows the text, the LLM's predicted label, and the reasoning field. Seeing why the AI chose a label makes the human verification 5x faster because you aren't reading the text from scratch; you're just auditing a decision.

Some hard-won tips for the prompt:

Define "Edge Case" behavior: Tell the LLM exactly what to do if the text doesn't fit any category. If you don't, it will hallucinate the closest match. I use: If no category fits, return label 'UNCERTAIN' and confidence 0.0.

Few-shot injection: While I started with zero-shot, adding just 3-5 gold-standard examples to the prompt reduces label variance by about 15% in my experience.

Batching for cost: Don't send one request per row. Group 10-20 samples into a single JSON array. It reduces API overhead and often helps the model maintain consistency across the batch.

The biggest gotcha is "label drift." If you label 10k rows this way, you'll notice the LLM's interpretation of a category might shift slightly over time. I solve this by randomly sampling 5% of the "high confidence" labels and manually auditing them. If the accuracy drops below 95%, I refine the prompt and re-run the batch.

This workflow transforms the developer's role from a data entry clerk to a quality assurance lead. You spend your time refining the prompt and auditing the edge cases rather than clicking checkboxes in a labeling tool.