AI Testing Methodologies

AI agents and LLMs require testing approaches that go beyond traditional software QA. Because these systems produce non-deterministic outputs, they can fail in subtle and unpredictable ways: hallucinating facts, following injected instructions, generating harmful content, or behaving differently across model versions. Effective AI testing combines multiple complementary methodologies to cover functional correctness, safety, security, and reliability.

Key testing approaches

Business Failures AI system failures that affect the business logic of the model.

Security Vulnerabilities AI system failures that affect the security of the model.

LLM Scan Giskard's automated vulnerability detection system that identifies security issues and business logic failures.

RAG Evaluation A comprehensive testing framework for Retrieval-Augmented Generation systems.

Adversarial Testing Testing methodology that intentionally tries to break or exploit models using carefully crafted inputs.

Human-in-the-Loop Combining automated testing with human expertise and judgment.

Regression Testing Ensuring that new changes don't break existing functionality.

Continuous Red Teaming Automated, ongoing security testing that continuously monitors for new threats and vulnerabilities.

Testing lifecycle

AI testing is iterative. Each stage feeds into the next, and findings from production monitoring often cycle back to inform new test cases.

1. Planning phase

Define what you’re testing and why. Identify the agent’s critical use cases, the risks specific to your domain (e.g., medical advice, financial transactions), and which failure categories matter most. Establish measurable success criteria, for example a maximum acceptable failure rate for correctness checks or zero tolerance for prompt injection in a regulated context.

2. Execution phase

Run your test suite using a combination of automated and manual approaches. Start with an automated red teaming scan to establish a security baseline, then build out test datasets covering functional, adversarial, and domain-specific scenarios. Use the annotation workflow to assign evaluation criteria to each test case.

3. Analysis phase

Review evaluation results across metrics, failure categories, and tags to identify patterns. Compare evaluations across agent versions to detect regressions. Focus remediation on the failure categories with the highest impact on your use case.

4. Remediation and continuous monitoring

Address identified vulnerabilities through prompt engineering, guardrails, model selection, or knowledge base updates. Re-evaluate to verify fixes. Then schedule automated evaluations and enable continuous red teaming so that new failures are caught as your agent, data, and models evolve.

Best practices

• Test at every stage: Evaluate during development (prompt iteration), deployment (CI/CD gates), and production (scheduled monitoring).
• Combine automated and manual testing: Automated scans catch known vulnerability patterns at scale; human red teamers find creative exploits that automated tools miss.
• Prioritize by risk: Not all failures are equal. Focus on the vulnerability categories that pose the greatest risk to your users and business.
• Version your test data: Keep test datasets versioned alongside your agent configuration so you can reproduce any evaluation.
• Close the loop: Convert production incidents and user feedback into new test cases to prevent recurrence.