?? Scaling LLM-Powered Data Labeling: Architecture Lessons from Labelling 5K Work Emails Llama3-70b ??

After successfully labelling 5,028 Enron emails as work/personal using the open-source Large Language Model, Llama3-70b, with Groq 's LPU-powered inference; I want to share battle-tested code patterns AND discuss why VCs are pouring $4.2B into AI data labeling (Scale AI $1B+, Snorkel $135M)! ??

While I worked on The Professional Filter: Machine Learning Approach to Work Email Detection with Ayush Gala and Vidhisha Kamat , I ran into the labor-intensive and costly process of manual data labelling.

After reviewing some literature on the topic, I turned to LLMs for automated data labeling, implementing 4 key innovations:

1?? Idempotent Processing:

Built a crash-resistant system that tracks file positions ensuring idempotence through the use of a last_index.txt file which records the last processed email to prevent reprocessing, enabling 24/7 unattended operation.

def populate_llm_prediction_emails_file(api_key):
    # Check if the CSV file already exists to decide on writing header
    csv_file_path = './data/llm/llm_prediction.csv'
    write_header = not os.path.exists(csv_file_path)
    print("Write header: ", write_header)

    # Load the last index if exists
    last_index_path = './tmp/last_index.txt'
    if os.path.exists(last_index_path):
        with open(last_index_path, 'r') as file:
            last_index = int(file.read().strip())

    # Open the CSV file for appending
    with open(csv_file_path, mode='a', newline='') as file:
        writer = csv.writer(file)
        if write_header:
            writer.writerow(['label'])  # Write the header if needed

        for i in range(last_index, len(data)):
            try:
                prompt = data.message[i][:6000]
                response = llama_groq.guarded_groq_call(prompt=prompt, api_key=api_key)
                print(response)
                # Write the validated output directly to the CSV
                writer.writerow([response.validated_output])
                # Save the current index
                with open(last_index_path, 'w') as index_file:
                    index_file.write(str(i + 1))
            except Exception as e:
                print(f"Error processing email at index {i}: {e}")
                return False
        return True        

2?? API Key Rotation:

Developed a smart API key cycling system using 10+ free Groq API keys to bypass rate limits, cut costs and ensure continuous operation:

retry_delay_seconds = 10
api_key_index = 0
while True:
    success = populate_llm_prediction_emails_file(llama_groq.api_keys['groq'][api_key_index])
    if success:
        print("All data processed successfully.")
        break
    else:
        print("An error occurred. Retrying with a different API key in {} seconds...".format(retry_delay_seconds))
        time.sleep(retry_delay_seconds)  # Delay before retrying
        api_key_index = (api_key_index + 1) % len(llama_groq.api_keys['groq'])        

3?? Custom LLM API wrapper for Groq:

    def my_custom_groq_api(self, api_key=None, **kwargs):
        """Custom LLM API wrapper for Groq.

        At least one of messages should be provided.

        Args:
            messages: The messages to be passed to the Groq API

        Returns:
            str: The output of the Groq API
        """
        messages = kwargs.pop("messages", [])
        groq_client = Groq(api_key=api_key)
        response = groq_client.chat.completions.create(model="llama3-70b-8192", messages=messages, max_tokens=len(messages)+1, **kwargs)
        print(response.choices[0].message.content)
        return response.choices[0].message.content        

4?? Prompt Engineering and Validation Guardrails:

Implemented strict LLM output validation ensuring only binary classifications (0 or 1) through the use of a system prompt (inspired from the Work Hard, Play Hard: Email Classification on the Avocado and Enron Corpora paper) and Regex pattern (^[01]$) matching validation (which would call the LLM again if the output didn't pass the validation step):

def guarded_groq_call(self, prompt='', api_key=None):
        """Guards the Groq API call to ensure the output is a binary classification."""
        guard = Guard().use(
            RegexMatch,
            regex=r"^[01]$",
            on_fail=OnFailAction.REASK
        )
        validated_response = guard(
            self.my_custom_groq_api,
            messages=[{"role":"system", "content":"If a message is about a social event inside the company, such as celebrating a new baby of an employee, or a career promotion, it belongs to the first category 1 (work-related). If a message is about a social event outside the company but still related to the company, such as a picnic (usually family members are invited), it belongs to the second category 0 (non-work-related). If a message is about a social event which is not related to the company such as a charity but company employees are encouraged to participate, it belongs to the second category 0 (non-work-related). If a message is too short to determine its category (or even empty), it should have the same category as the message it is responding to, or the message it is forwarding. If a message is ambiguous, try to read other messages in the thread to clarify. If a message is spam or in the rare case that the first message of a thread is very short or empty, say 1 (work-related)"},
                     {"role":"user", "content":"For the following email, please classify it as 1 (work-related) or 0 (non-work-related). Do not include any other text in your response: " + prompt}],
            api_key=api_key
        )        

?? Results:

• 5,028 emails processed
• Zero data loss incidents
• Continuous operation for over 18+ hours

?? Key Learning:

• The success of LLM-based systems isn't just about prompt engineering - it's about building robust, production-ready infrastructure around the AI!

?? Essential Papers on LLM Data Labeling

• Large Language Models for Data Annotation and Synthesis: A Survey
• MEGAnno+: A Human-LLM Collaborative Annotation System

?? Industry-Leading Implementations:

• Scale AI ($325M Series E): Automated labeling platform
• Snorkel AI ($85M Series C): Programmatic labeling
• Labelbox ($110M Series D): LLM-powered annotation

?? Key Takeaways:

• LLMs reduce labeling costs by 60-80%
• Human-in-the-loop still crucial for edge cases
• Prompt engineering significantly impacts accuracy
• Idempotent systems essential for production

?? Isn't training a neural network on LLM-labeled data like a snake eating its own tail?

?? The Ouroboros Concern:

• The practice of training neural networks using data labeled by large language models (LLMs) has emerged as both a pragmatic solution to data scarcity and a subject of intense methodological debate.
• This approach, while computationally efficient, raises fundamental questions about the long-term viability of machine learning systems that rely on their own synthetic outputs.
• Drawing parallels to the Ouroboros—the ancient symbol of a serpent consuming its own tail—we have to consider how recursive training cycles impact model performance, data integrity, and the broader machine learning ecosystem.
• The transition to synthetic data generation marks a fundamental shift in machine learning methodology, enabling rapid iteration at scale but introducing new forms of statistical dependency between models and their training data.

??Three Distinct Degradation Modes in Recursively Trained Models

• Conceptual Drift: Gradual shift in feature space alignment
• Variance Collapse: Loss of minority class representation
• Error Amplification: Reinforcement of systematic mistakes These phenomena collectively create what researchers term the "Stochastic Echo Chamber Effect", where models become increasingly confident in their errors while losing contact with ground truth referents.

??Epistemological Challenges in Synthetic Learning

The Ouroboros paradigm raises fundamental questions about the nature of machine knowledge:

• Can models grounded in synthetic data develop genuine understanding?
• How do we define "truth" in systems detached from empirical observation?
• What constitutes valid justification when all training examples derive from prior model outputs?

Recursive training creates hyperreal epistemologies—self-referential systems of knowledge validation that lack external anchors. This mirrors postmodern critiques of media ecosystems, where the distinction between reality and simulation becomes increasingly blurred.

??My Take

It's not a perfect circle - it's more like a spiral. Each iteration can add value if we:

• Validate against human ground truth
• Use LLMs as one tool in a diverse labeling pipeline
• Maintain human oversight for critical cases

?What's your take on LLM-powered data labeling?

?Have you implemented similar systems?

?Let me know in the comments!

#AIEngineering #MachineLearning #DataScience #LLM #Groq