Universal-LLM-Output-Auditor

Universal LLM Output Auditor

The Universal LLM Output Auditor is an open-source framework designed to solve one of the most significant challenges in generative AI: Trust and Verification.

As Large Language Models (LLMs) are integrated into high-stakes environments, the need for automated, objective, and scalable auditing becomes paramount. This tool leverages the high-speed, long-context capabilities of the Gemini 1.5 Flash API to act as a "critic" model, evaluating the outputs of other LLMs against rigorous quality and safety benchmarks.

🚀 Overview While many evaluation frameworks are limited to specific domains, this project provides a generalized infrastructure to verify responses from any LLM. It moves beyond simple keyword matching to perform deep semantic and logical analysis of model behavior.

Key Capabilities Cross-Model Auditing: Evaluate outputs from OpenAI, Anthropic, Meta (Llama), or local models using a unified evaluation layer.

Instruction Adherence: Quantifiably measures how well a model followed complex, multi-part system prompts.

Factual & Logical Verification: Cross-references generated content for internal consistency and logical fallacies.

High-Speed Processing: Optimized for the Gemini 1.5 Flash architecture to allow for real-time or bulk auditing of massive datasets.

Streamlit Integration: Features a clean, interactive web interface for manual "spot-check" auditing and data visualization.

🛠️ Technical Stack Core Engine: Python 3.10+

Primary Auditor: Google Gemini 1.5 Flash (via google-generativeai)

Interface: Streamlit

Data Handling: Pandas & NumPy

API Management: Asynchronous request handling for batch processing

⚙️ Architecture & Workflow The system follows a "Judge-Model" pattern:

Input Phase: The auditor receives the original prompt and the "Candidate LLM" response.

Criteria Selection: The user defines evaluation rubrics (e.g., Ethical Compliance, Technical Accuracy, or Tone).

The Audit: Gemini 2.5 Flash parses the context, identifies potential hallucinations or errors, and assigns a normalized score.

Reporting: The system generates a breakdown of "Pass/Fail" flags and a final reliability report.

Built With

• jupyter-notebook