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AI Application Quality

AI Testing & Generative AI Evaluation

AI applications require a different approach to quality. Traditional functional testing alone cannot fully evaluate whether an AI-enabled experience is accurate, reliable, safe and resilient to unexpected inputs. OPAL helps teams test AI and GenAI applications across functional, behavioural and security dimensions.

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AI Evaluation Matrix
Test Dimensions
Accuracy
→
Safety & Drift
→
Release

Prompt variation • Hallucination analysis • Adversarial guardrails

Hallucination Checks Prompt Injection Defense Data Leakage Prevention Behavioural Quality
Methodology

Comprehensive Behavioural & Security Assessment

OPAL assesses AI application behaviour for accuracy, reliability, consistency and safety. The objective is not simply to confirm that an AI system works—it is to understand how it behaves when users, data and conditions vary.

Scenarios Tested

Functional & Behavioural Quality

  • Hallucination-Focused Scenarios: Stress-testing factuality, grounding, and citation integrity against complex queries.
  • Adversarial Inputs & Edge Cases: Injecting malformed, ambiguous, and boundary conditions to assess error handling.
  • Consistency & Determinism: Evaluating response consistency across repeated queries and system prompt revisions.
Security Dimensions

Safety & Security Vulnerabilities

  • Prompt Injection & Jailbreak: Verifying defenses against direct and indirect prompt overrides.
  • Data Leakage Risks: Ensuring sensitive PII, business credentials, and training data remain protected.
  • Model Drift & Regression: Ongoing verification as foundation models update or temperature parameters shift.
Hybrid Process

Expert-Led & Automated Evaluation

Where relevant, OPAL combines expert-led evaluation with automated approaches to generate, execute and analyse broader test scenarios. This helps teams create repeatable validation processes that can evolve as prompts, models, integrations and application workflows change.

Target Relevance

Who Needs AI Testing?

AI Testing is especially relevant for organizations deploying customer-facing assistants, AI-enabled workflows, GenAI features or AI agents where unpredictable behaviour can affect trust, security or business outcomes.

FAQs

AI Testing Questions & Answers

Key information regarding engagement scope and validation techniques.

What is AI testing? +
AI testing evaluates AI and GenAI applications for accuracy, reliability, safety, security and behavioural quality. Depending on the application, this can include hallucination, prompt injection, data leakage and adversarial behaviour testing.
How does AI testing differ from traditional QA? +
Traditional functional testing alone cannot fully evaluate non-deterministic outputs. AI testing evaluates probabilistic responses, jailbreaks, prompt degradation, and behavioural drift across diverse user conditions.
How do we get started? +
Start with a focused Proof of Concept (POC) or AI Quality Assessment. We evaluate your current prompts, models, and workflows to identify key vulnerabilities and establish a repeatable validation benchmark.
Ready to Validate Your AI Feature?

Talk to OPAL about your AI application and quality goals.

Identify risks such as hallucination, prompt injection and unexpected model behaviour before they affect users.

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