Introduction
As artificial intelligence evolves, Large Language Models (LLMs) are emerging as powerful tools for generating human-like content, translating languages and handling complex queries.
To harness this power responsibly, HCLTech offers AssureAI – a comprehensive suite of testing services designed to ensure the quality and trustworthiness of your LLM-powered applications. Our services include functional testing, performance testing and responsible AI testing. By conducting rigorous LLM testing, organizations can build trust in the capabilities and constraints of their AI models, driving both innovation and ethical AI development.
Service Offerings
Functional Testing
- Generation quality (Readability)
- Completeness of response
- Exploratory testing, integration testing
- Regression testing
- Usability testing: Ul testing, error handling, context awareness, non-text prompt
Compatibility Testing
- Multi-device testing
- OS testing
- Browser and apps
- Multi-platform
- Multiple markets/Multilingual
- Interoperable testing
Responsible Al Testing
- Bias
- Fairness
- Toxicity
- Hallucination
- Red teaming
Performance Testing
- Speed: Processing speed, response time
- Scalability: User load, traffic and large data volume
- Memory capture
Certification Testing
- Certify LLM apps and extensions based on defined marketplace policies
- Partner enablement support
- Post-certification support
Metrics Driven Testing
- Correctness testing (accuracy, factual and semantic)
- Similarity testing
- Evaluator model testing
Security Testing
- Robustness: Unauthorized user, prompt injection
- Compliance: Detect bias, handle offensive content
Accessibility Testing
- LLM app accessibility testing
- MASS and WCAG rules/ guidelines
- Compliance and remediation
- Usability studies
- VPAT certification
Unit Testing
- Property-based testing
- Example-based testing
- Auto-evaluation
- Adversarial testing
Solutions
PyRIT
Open-access automation framework crafted to empower security professionals and machine learning engineers to proactively identify risks in their generative AI systems
TrustifAI
Evaluates the risk assessment for AI applications on explainable AI, fair AI, secure AI and ethical AI
RAIPilot
Copilot bot designed to automate assessment documents, powered by the latest Responsible AI (RAI) standards, checklists and guidelines
Content Safety
Moderates diverse content leveraging
Azure OpenAI moderation APIs or other tools for enhanced user experience
Responsible Toolkit
Aids in comprehensive model debugging and decision-making and analyzes and compares cohorts for informed business actions
RAGAS
Framework for evaluating Retrieval Augmented Generation (RAG) pipelines, augmenting the LLM’s context using external data
DeepEval
Open-source framework for extensive LLM evaluation, beyond traditional metrics for a holistic performance assessment
Prompt Flow
Simplifies AI application development, from prototyping to deployment, ensuring streamlined processes
Test areas
Our LLM Testing areas encompass a wide range of offerings designed to ensure the quality, reliability, and performance of your LLM-powered applications.
These areas include Transparency, Ethical AI, Security, Prompt injection, Privacy, Toxicity, Fairness, Prompt poisoning, Jailbreaking, Training data poisoning, Data leakage, Groundless detection, Performance and efficiency, and Consistency.
Case Studies
Ensuring the reliability and quality of a Copilot AI tool across multiple products
Over nine months, we identified and resolved more than 1200 bugs, of which 30% were classified as critical. The year-long comprehensive program included functional testing, prompt validation, jailbreak and performance testing.
Ensuring the reliability and quality of AI Force application
Our thorough testing program ensured that the AI Force application adheres to the highest standards of quality, reliability and security. We completed the impact assessment, PII masking, role-based access and red teaming.
