LLMCheck

LLM Hallucination & Drift Detection — Coming Soon

A Python toolkit to verify LLM outputs for hallucinations, factual accuracy, and model drift over time.

What This Package Is

LLMCheck is an upcoming utility package designed to help developers:

• Detect hallucinations in LLM-generated content
• Verify factual accuracy against source documents
• Monitor model drift across deployments and versions
• Score output reliability for production systems
• Alert on consistency degradation in LLM pipelines

This package is being developed by Haiec as part of a broader AI governance infrastructure.

Why This Namespace Exists

The llmverify namespace is reserved to provide developers with essential LLM quality assurance tools. As LLMs become critical infrastructure, verifying their outputs is non-negotiable.

This package will provide:

• Hallucination scoring algorithms
• Source-grounded verification
• Temporal drift analysis
• Confidence calibration utilities
• Integration with popular LLM frameworks (LangChain, LlamaIndex)
• Real-time monitoring hooks

Installation

pip install llmcheck

Placeholder Example

import llmcheck

# Check package status
print(llmcheck.__version__)  # '0.0.1'
print(llmcheck.__status__)   # 'placeholder'

# Detect hallucination (placeholder)
result = llmcheck.detect_hallucination(
    output="LLM generated this output",
    context="Original source context"
)
print(result["message"])

# Detect drift (placeholder)
drift_result = llmcheck.detect_drift([
    "output from day 1",
    "output from day 2",
    "output from day 3"
])
print(drift_result["message"])

Roadmap

• Hallucination detection engine
• Source-grounded verification
• Semantic drift scoring
• Confidence calibration
• LangChain integration
• LlamaIndex integration
• Real-time monitoring API
• Alerting webhooks
• Dashboard visualization hooks

License

MIT © 2025 Haiec

Contact

For early access or partnership inquiries, reach out to the Haiec team.