How to Log Your LLMs While Respecting GDPR: Developer Guide

With the massive adoption of LLMs (Large Language Models) in business, the question of interaction logging becomes critical. How do you trace exchanges for debugging and improvement while respecting GDPR and user privacy?

Why log LLM interactions?

• Debugging — Understand why the model gave an unexpected response
• Improvement — Identify problematic use cases
• Audit — Trace AI decisions
• Costs — Monitor token consumption
• Security — Detect abuse or injection attempts

HILT: Open-source solution

HILT (Human-AI Log Tracing) is a Python library designed for GDPR-friendly LLM logging.

Installation

pip install hilt-python

Basic usage

from hilt import instrument, uninstrument
from openai import OpenAI

# Enable logging
instrument(backend="local", filepath="logs/chat.jsonl")

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)

# Disable logging
uninstrument()

Conclusion

LLM logging is essential for monitoring and improvement but must be done responsibly. Privacy-first solutions like HILT allow tracing interactions without compromising GDPR compliance.

Resources:

• HILT on GitHub
• HILT on PyPI