Service Overview
AI & LLM Testing
Secure, reliable, and hallucination-free AI applications.
The Approach
Testing generative AI and Large Language Models (LLMs) requires a fundamental shift from traditional QA. Because AI output is non-deterministic (it changes every time), standard pass/fail assertions don't work. I specialize in validating complex AI behaviors, specifically focusing on mitigating hallucinations, ensuring prompt adherence, and testing data privacy boundaries.
Core Capabilities
Prompt Boundary Testing
Intentionally trying to break or jailbreak the model to ensure it adheres to safety guidelines.
Context Window Validation
Testing how the model handles massive data inputs and multi-turn conversations without losing context.
RAG (Retrieval-Augmented Generation) Testing
Ensuring the AI accurately cites information from your database rather than hallucinating facts.
Algorithmic Bias Auditing
Checking the model's outputs for unintended biases across different demographic inputs.
Business Value
Deploy your AI features with absolute confidence. Prevent PR disasters and loss of user trust caused by hallucinated data or inappropriate AI responses.
Need expertise in AI & LLM Testing?
Let's discuss how we can implement a robust testing strategy for your specific use case.