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Q-Aware Labs

Advancing AI Quality & Safety through Rigorous Testing

Q-Aware Labs is dedicated to improving the reliability, safety, and performance of AI systems through innovative testing frameworks, ethical safeguards, advanced prompt engineering, and data quality standards.

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RESEARCH FOCUS

Core Research Areas

Q-Aware Labs is my space to develop frameworks, methodologies, and tools to address key challenges in AI quality assurance and responsible deployment.

✅

AI Quality Assurance

Comprehensive frameworks and methodologies for AI system testing focusing on performance, robustness, consistency, and behavioral validation.

• Performance validation across scenarios
• Robustness testing against edge cases
• Consistency evaluation across versions

🛟

Ethical AI Safeguards

Developing and implementing safeguards that ensure AI systems operate within ethical boundaries and respect human values.

• Bias detection and mitigation strategies
• Fairness metrics and monitoring
• Transparency and explainability tools

📝

Prompt Engineering Excellence

Researching and developing best practices for prompt engineering to enhance AI system reliability and performance.

• Systematic prompt testing methodologies
• Prompt optimization techniques
• Version control for prompts

🔬

AI Testing Automation

Developing advanced automation frameworks for continuous and comprehensive AI system testing.

• Automated test case generation
• Continuous integration for AI systems
• Regression testing frameworks

✍️

Data Quality for ML

Helping organizations maintain high-quality training data through validation frameworks and quality monitoring.

• Data validation frameworks
• Dataset bias analysis
• Data cleaning and preprocessing tools

⚔️

Adversarial Testing

Researching techniques to assess AI system robustness against adversarial inputs and edge cases.

• Advanced prompting techniques
• Robustness against subtle changes
• System boundary testing

LATEST WORK

Featured Experiments

Explore our latest research experiments, tools, and frameworks aimed at improving AI quality and reliability.

LLM Evaluations

LLM Evaluations

Evaluating Web Search Performance Across Top AI Assistants

Exploring comprehensive insights about how AI Assistants search the web and generate responses based on their findings.

AI Assistants AI Web Search Gemini Flash

AI Bias Detection Tool

Prompt Engineering

VeriBot: Intelligent AI Response Testing Automation

VeriBot is a lightweight, configurable framework for automated testing of AI language models.

Prompt Engineering Python LLM Evaluation

Automated Test Generation

Testing Automation

Automated Edge Case Generator

An intelligent system that automatically generates edge case test scenarios for machine learning models based on data patterns.

PyTorch FastAPI Docker

View All Experiments

ABOUT

About Q-Aware Labs

Our mission is to advance the quality, safety, and reliability of artificial intelligence systems through rigorous research and practical innovation.

Q-Aware Labs was founded with a clear mission: to help organizations build more robust, reliable, and responsible AI solutions through innovative testing methodologies, quality frameworks, and ethical safeguards.

As AI systems become increasingly integrated into critical domains and everyday applications, the need for comprehensive quality assurance approaches grows. Traditional software testing methodologies often fall short when applied to AI systems, which deal with probabilistic outputs, complex data dependencies, and potential ethical implications.

Our lab focuses on developing frameworks, tools, and methodologies that address these unique challenges, helping organizations ensure their AI systems perform reliably across diverse scenarios, remain robust against edge cases, maintain consistency across versions, and operate within ethical boundaries.

Through a combination of research, experimentation, and practical application, we aim to advance the field of AI quality assurance and contribute to the development of safer, more reliable AI systems that can be trusted in real-world contexts.

antony.garcia@qawarelabs.com