GenAI SME - UOB

Core Responsibilities of a Gen AI Testing SME

1. Test Strategy & Planning

• Define comprehensive testing strategies tailored for Gen AI models (LLMs, diffusion models, etc.).
• Identify key testing dimensions: accuracy, relevance, coherence, bias, toxicity, hallucination, and safety.
• Develop test plans for different stages: pre-training, fine-tuning, prompt engineering, and deployment.

2. Test Case Design & Automation

• Design test cases for both deterministic and non-deterministic outputs.
• Create benchmark datasets and golden sets for evaluation.
• Develop automated testing pipelines using tools like LangChain, PromptLayer, or custom frameworks.

3. Evaluation Metrics & Analysis

• Define and apply appropriate evaluation metrics (e.g., BLEU, ROUGE, perplexity, factual consistency).
• Analyze model outputs for hallucinations, bias, and harmful content.
• Conduct A/B testing and human-in-the-loop evaluations.

4. Prompt & Scenario Testing

• Test prompt robustness across variations, edge cases, and adversarial inputs.
• Validate prompt templates and chaining logic in RAG or agent-based systems.
• Ensure consistency and reliability across different user intents and contexts.

5. Risk & Compliance Testing

• Validate adherence to responsible AI principles (fairness, transparency, accountability).
• Test for compliance with data privacy laws (e.g., GDPR, PDPA) and industry regulations.
• Identify and mitigate risks related to model misuse or unintended behavior.

6. Tooling & Infrastructure

• Set up and maintain testing environments for Gen AI models (cloud-based or on-prem).
• Integrate testing into CI/CD pipelines for continuous validation.
• Leverage synthetic data generation and simulation tools for scalable testing.

7. Collaboration & Reporting

• Work closely with Testing Domain Lead, data scientists, ML engineers, product teams.
• Document test results, issues, and recommendations clearly.
• Provide feedback loops to improve model training, fine-tuning, and deployment.