LLM Quality Assurance
Comprehensive testing, validation, and monitoring for enterprise AI applications, ensuring accuracy, reliability, and compliance at scale.
What is LLM Quality Assurance?
LLM Quality Assurance is Divinci AI's comprehensive solution for ensuring the reliability, accuracy, and safety of enterprise AI applications. Traditional software QA methods fail to address the unique challenges of LLM-based systems, including hallucinations, bias detection, and non-deterministic outputs.
Our platform provides a complete testing and validation framework specifically designed for AI systems, with automated testing, continuous monitoring, and detailed analytics that help organizations maintain trust in their AI applications. We employ a multi-layered approach combining prompt testing, output validation, factual verification, and behavioral analysis to provide comprehensive quality assurance.
Whether you're developing customer-facing AI assistants, implementing internal knowledge systems, or deploying specialized AI tools, our LLM Quality Assurance platform ensures your applications meet the highest standards of quality and reliability while maintaining compliance with regulatory requirements.
Key Benefits
LLM Quality Assurance
Comprehensive testing and validation to ensure your AI delivers accurate, reliable, and compliant responses every time.
Minimize AI Hallucinations
Significantly reduce factual inaccuracies with our comprehensive hallucination detection and prevention system.
Ensure Compliance & Safety
Maintain regulatory compliance and brand safety with automated testing for bias, toxicity, and policy adherence.
Accelerate Testing Cycles
Dramatically reduce QA time with automated testing that simulates thousands of user interactions.
Continuous Improvement
Leverage real-time analytics and user feedback to continuously refine your AI systems.
Comprehensive Reporting
Detailed analytics and insights to track quality metrics and demonstrate compliance to stakeholders.
Feature Details
Automated Testing
Our comprehensive testing framework automatically evaluates your AI applications across multiple dimensions, identifying potential issues before they impact users.
- Test Case Generation: Automatically generate thousands of test cases based on usage patterns and edge cases
- Red Teaming: Simulate adversarial interactions to identify potential vulnerabilities and edge cases
- Regression Testing: Ensure changes to prompts or models don't introduce new issues or affect existing functionality
- Behavioral Testing: Verify consistent AI behavior across similar inputs with different phrasing or contexts
- Compliance Validation: Automatically check against industry-specific regulations and policies
- Performance Testing: Measure response times, token usage, and other performance metrics under various loads
- Integration Testing: Validate AI components work correctly with other systems and data sources
Validation Engine
Our validation engine evaluates AI outputs against multiple quality dimensions, ensuring accuracy, relevance, and safety of all generated content.
- Factual Verification: Check AI-generated claims against trusted knowledge bases and sources
- Hallucination Detection: Identify and flag generated content not supported by available context
- Bias Analysis: Detect and measure various forms of bias in AI outputs
- Toxicity Screening: Identify harmful, unsafe, or offensive content
- Context Relevance: Ensure responses are appropriate and relevant to the user's query
- Formatting Validation: Verify outputs conform to expected structure and format requirements
- Citation Accuracy: Validate that sources and citations in responses are accurate and exist
Continuous Monitoring
Our monitoring system provides real-time insights into AI application performance, enabling proactive quality management and continuous improvement.
- Real-time Analytics: Comprehensive dashboards showing quality metrics, usage patterns, and potential issues
- Anomaly Detection: Automatically identify unusual patterns or degradation in AI performance
- User Feedback Analysis: Collect and analyze explicit and implicit user feedback
- Quality Trending: Track quality metrics over time to identify gradual shifts or degradation
- Alert System: Immediate notifications when quality thresholds are breached
- Audit Trails: Comprehensive logs for compliance and troubleshooting
- Improvement Recommendations: AI-generated suggestions for enhancing response quality
Implementation
Quality Assessment & Planning
Our team conducts a comprehensive assessment of your current AI applications and quality processes. We identify key quality metrics, potential risks, and compliance requirements specific to your industry and use cases, creating a tailored implementation plan.
Testing Framework Implementation
We configure and deploy our testing framework, integrating with your existing development and deployment processes. Initial test suites are created based on your requirements, and baseline measurements are established for ongoing quality assessment.
Production Monitoring & Optimization
With the testing framework in place, we implement continuous monitoring for production systems, providing real-time visibility into quality metrics. Our team helps establish quality gates for deployment processes and provides ongoing optimization guidance.
Success Stories
Global Financial Institution
Significantly reduced compliance risks while accelerating AI deployment
A leading global bank needed to ensure their customer-facing AI assistant consistently provided accurate financial advice while strictly adhering to regulatory requirements. Our LLM Quality Assurance platform enabled comprehensive testing for factual accuracy, regulatory compliance, and bias, while reducing their QA cycle time from weeks to days.
Request Case Study →"Divinci's LLM Quality Assurance platform transformed our ability to deploy AI with confidence. We can now detect potential compliance issues before deployment and continuously monitor for quality, which has been essential for maintaining regulatory compliance in our industry."
— Sarah Johnson, Chief Risk Officer
Healthcare Technology Provider
Achieved exceptional accuracy in medical information with comprehensive testing and continuous verification against trusted medical sources.
Request Details →E-Commerce Platform
Significantly reduced customer support escalations by implementing comprehensive testing for their AI shopping assistant.
Request Details →Government Agency
Ensured complete policy compliance while substantially reducing testing time for citizen service AI applications.
Request Details →Frequently Asked Questions
Our hallucination detection system uses a multi-layered approach:
- Source Verification: We compare AI-generated statements against reliable knowledge sources, including your organization's knowledge base and trusted external sources
- Semantic Analysis: Advanced NLP models analyze output coherence and logical consistency
- Uncertainty Detection: We identify patterns in language that indicate uncertainty or fabrication
- Statistical Validation: Multiple runs with similar inputs help detect inconsistent responses that may indicate hallucinations
- Contextual Verification: We ensure outputs are properly grounded in provided context
This comprehensive approach catches the vast majority of potential hallucinations before they reach users, with continuous improvements as the system learns from new examples. For regulated industries, we provide specialized verification against industry-specific knowledge sources and regulations.
Traditional testing relies on exact matching, which doesn't work for LLMs that produce different but equally valid responses to the same input. Our system addresses this through:
- Statistical Testing: Running multiple test iterations and analyzing the distribution of responses
- Semantic Evaluation: Testing whether responses have the same meaning rather than identical wording
- Constraint Verification: Checking that outputs meet defined constraints (format, included information, etc.)
- Probabilistic Assertions: Testing that responses fall within acceptable probability distributions
- Property-based Testing: Verifying that outputs maintain critical properties regardless of exact wording
Our approach recognizes that AI outputs exist within a range of acceptable variations. We help you define what constitutes acceptable variation for your specific use cases and ensure your AI consistently stays within those boundaries.
Yes, our LLM Quality Assurance platform includes specialized compliance modules for various industries:
- Financial Services: FINRA, SEC, MiFID II, and other financial regulations
- Healthcare: HIPAA, HITECH, FDA requirements for medical information
- Legal: Legal advice boundaries, jurisdiction-specific requirements
- Government: ADA compliance, accessibility standards, service requirements
- Retail/E-commerce: FTC guidelines, product claim accuracy, pricing transparency
Each industry module includes pre-built test suites, specialized verification sources, and reporting templates aligned with regulatory requirements. We also provide customization services to address your organization's specific compliance policies and regulatory obligations. All compliance testing is continuously updated as regulations evolve.
Our LLM Quality Assurance platform offers multiple integration points with your development workflow:
- CI/CD Integration: Automated testing as part of your build and deployment pipeline
- API Access: Programmatic access to all testing capabilities for custom integrations
- Developer Tools: IDE plugins and CLI tools for testing during development
- Pre-built Integrations: Connectors for common development platforms (GitHub, GitLab, JIRA, etc.)
- Quality Gates: Configurable quality thresholds for deployment approval
Our implementation team will work with your development and DevOps teams to create a tailored integration that fits your existing processes. We focus on minimizing disruption while maximizing the value of comprehensive quality assurance. The system can be deployed gradually, starting with specific high-priority applications or test environments before expanding to your full AI portfolio.
Maintaining relevant test coverage for rapidly evolving AI applications is a key challenge. Our approach includes:
- Usage-Based Test Generation: Automatically creating new test cases based on actual user interactions
- Coverage Analysis: Identifying gaps in test coverage as applications evolve
- Continuous Learning: Updating test expectations based on approved AI responses
- Regression Protection: Preserving tests for critical functionality while adapting to intentional changes
- Anomaly Detection: Identifying unexpected behavior changes that might require new tests
The system provides regular recommendations for test coverage improvements based on application changes, user feedback, and emerging usage patterns. This ensures your quality assurance evolves in parallel with your AI capabilities, maintaining comprehensive coverage without requiring constant manual updates to test suites.
Ready to Ensure AI Quality and Reliability?
Schedule a demo to see how our LLM Quality Assurance platform can help you deliver trusted, accurate AI experiences.