OVERVIEW

Large Language Models (LLMs) are transforming how organizations operate, offering new ways to simplify tasks, improve decision-making, and address complex challenges.
In environments where accuracy, transparency, and compliance are critical, a resilient process for testing and evaluating these powerful tools is essential.
A robust Test and Evaluation (T&E) Framework not only ensures that the LLMs used are reliable and secure, but also facilitates systematic assessment of safety and performance. Integrating adaptive AI guardrails into this framework further creates a safe and resilient mechanism to deploy LLMs aligned with organizational priorities.
Together, they uphold the highest standards in AI ethics and integrity- aligned with mandatory business and compliance requirements.

CLIENT

A leading Technology Service Provider supporting government agencies, with experience spanning sectors including healthcare, cybersecurity, and environmental management needed a robust T&E structure to ensure proactive risk management, compliance, governance, process-driven evaluation, and continuous monitoring.
They partnered with Quantrium to build an end-to-end framework for Generative AI Large Language Models (LLMs) by defining the standard procedures, benchmarks, and tools for testing and evaluation, aligning these processes with organizational goals and risk tolerance.

CHALLENGES

Prompt Sensitivity

The evaluation metrics showed variation based on how the prompts were phrased, leading to inconsistent outcomes.

Construct Validity

Defining satisfactory answers across diverse use cases remained challenging.

Contamination

Bias detection in models was difficult, especially when discerning ideological leanings.

Lack of Standardization

Diverse evaluation benchmarks led to inconsistencies across studies.

Adversarial Attacks

Assessing model robustness against manipulative prompts was critical.

THE QUANTRIUM WAY FORWARD

Parameters used by Quantrium to evaluate the LLMs

Performance Evaluation

To assess the accuracy, speed, and efficiency of AI models.

Security Assessment

To ensure data privacy and protection against adversarial attacks.

Compliance Verification

To verify adherence to federal regulations and standards.

Usability Testing

To evaluate the user-friendliness and accessibility of AI tools.

Integration Capability

To test the interoperability with existing agency systems.

Bias and Fairness Assessment

To assess and detect biases in model outputs.

Evaluation Transparency

To ensure transparency in the framework to promote confidence and trust.

Framework Agility

To deliver an agile framework to seamlessly adapt to the evolving GenAI landscape.

Customized Evaluation Methodologies

• Implementation of Industry benchmarks, automated metrics (like Perplexity, Bleu, Rouge), and ROI measures to assess AI performance.
• Emphasis on guardrails, steerability, safety, security, and transparency as models scaled in their capabilities.
• Alignment with frameworks for AI safety and compliance.

Frameworks and Tools

• LEval
• Prometheus
• GPTEval Framework
• BIG-bench

IMPACT

• The establishment of a comprehensive Testing &Evaluation (T&E) Framework with a robust risk management foundation customized to the client’s mission and organizational context.
• Effective implementation of AI Guardrails within predefined ethical and operational limits.
• Risk mitigation practices aligned with compliance and governance requirements.
• Enhanced security throughout the system development life cycle for proactive vulnerability detection and mitigation.
• Management of AI risks proactively, ensuring operational reliability.
• Implementation of frameworks to strengthen organizational control over AI risks by embedding ethical and compliance guardrails directly into systems’ operational fabric, enhancing trustworthiness and regulatory adherence.

TECHNOLOGY STACK

Python

FastAPI

MongoDB

VueJS

Pytorch