The Need to Rethink Testing in the Age of GenAI
The rise of Generative AI alongside traditional ML has fundamentally changed the risk landscape for AI applications:
- A shift from quantitative outputs (boolean, numeric) to qualitative outputs (text, images, code).
- An exponential increase in model complexity (measured in training data volume or parameter count).
- Data scientists no longer have the monopoly on handling model outputs: all employees can engage with AI (through e.g. chat or voice).
This evolution introduces novel risks (prompt attacks, hallucinations, and toxic outputs) while amplifying existing concerns around data bias, transparency, and model quality. Such profound changes demand a comprehensive reimagining of AI system testing methodologies.
Challenges Facing GenAI Testers
Many organizations increasingly rely on third-party LLMs. Even with thorough due diligence during model selection (evaluating performance, cost, transparency, and reliability), significant testing challenges remain:
Any single framework or benchmark is typically insufficient:
- Automated red-teaming solutions like HarmBench often have a rather specific scope, creating false security.
- Frontier-level benchmarks struggle to stay ahead of technological advancement over time, as is illustrated by the speed at which the score of leading models on Humanities Last Exam improves.
- Model specialization has led to benchmark fragmentation, as evidenced by Stanford's Holistic Evaluation of Language Models (HELM).
This creates a complex evaluation landscape:
- Model providers selectively highlight benchmarks that support their marketing narratives.
- Public benchmarks require scrutiny, as illustrated by the LM Arena controversy.
In addition, benchmarks often fail to reflect real-world performance:
- Defining meaningful human benchmarks presents significant challenges (arXiv:2506.13776)
- Trusted LLMs introduced into RAG pipelines create new vulnerabilities (arXiv:2504.18041)
- LLM-as-judge automation can silently fail, undermining validity (arXiv:2509.20293)
Functional safety of GenAI
AI needs to operate correctly and safely, particularly in environments where their outputs could impact human safety or critical systems. Safety should cover intended applications as well as foreseeable misuse. The versatility and adaptability of GenAI make this a strong requirement that may not be easy to satisfy.
When combined with the heightened real-world impact of agentic AI systems and the potential for error amplification through automation, these factors underscore the critical need for robust testing strategies focused on risk mitigation, user value, and efficient resource allocation across conversational, RAG, and function-calling components.
Five Strategic Approaches for Effective GenAI Testing
1. Define Quality Across the Full User Journey
Move beyond basic accuracy metrics to establish quantifiable quality definitions that span entire user interactions and directly tie testing to business value. There is no panacea. You will need to adapt based on the type of AI that you are implementing.
- Knowledge retreival: Quality means Grounding and Completeness. The system must consistently cite information from its knowledge base and use the knowledge it is given, minimizing costly hallucinations or factual errors. Example metrics: Groundedness and Knowledge Coverage Scores.
- Conversation flow: Quality means Coherence and Completion. The system must maintain context across multiple turns and guide users to successful outcomes. Example metrics: conversation success rate, tone of voice, response alignment.
- Function calling : Quality means Safety and Precision. The model must correctly interpret intent to invoke appropriate functions with precise arguments. Example metrics: function call precision, succesful completion rate, opened disputes.
2. Prioritize Testing by Risk and Cost of Failure
Allocate your most intensive testing resources to the highest-impact failure modes.
- Tier 1: High-Risk Scenarios: Focus on function calling (which triggers external actions) and high-stakes RAG domains (legal/financial advice). Deploy human-in-the-loop testing for adversarial attacks and qualitative review of potentially harmful responses.
- Tier 2: High-Volume Scenarios: Concentrate on common user queries and RAG topics using an automated "golden set" to ensure consistent performance and catch regressions from prompt or data updates. Make sure tocontinuously review and update testing benchmarks to prevent obsolescence as user needs and model capabilities evolve.
- Tier 3: System Resilience: Stress test the entire pipeline to ensure it handles anticipated user load within acceptable latency parameters. Validate that flooding the context window does not allow for vulnerabilities to be exploited.
3. Implement Comprehensive Automated Guards
Establish automated checkpoints throughout your pipeline to verify component quality before final output generation.
- Retrieval Guard: Validate that retrieved documents semantically align with user queries before LLM processing, triggering predefined fallback strategies when retrieval quality falls below thresholds.
- Function Call Guard: Validate generated JSON payloads against strict API schemas to catch hallucinated parameters or incorrect formatting before external system interaction.
- Safety/Harm Guard: Filter final outputs through dedicated content moderation systems for toxicity and bias detection before delivery.
- End-to-End Tracing: Implement comprehensive tracing across the entire processing flow to identify failure points and performance bottlenecks with precision.
Frameworks and best practices in this space are still fast-evloving, as illustrated by the recent release of Anthropic's Petri for automated auditing of chat safety.
4. Apply Code-Like Testing to Your Knowledge Base
Your system's effectiveness depends fundamentally on knowledge base quality, deserving the same rigorous testing as application code.
- Data Quality Testing: Verify that ingestion and chunking strategies preserve information integrity. Test whether specific facts can be retrieved from appropriate chunks rather than being buried in irrelevant content.
- Embedding Model Evaluation: Systematically compare embedding model performance to ensure semantic capture of domain-specific terminology and concepts.
- Negative Testing (Knowledge Gap): Deliberately test with questions outside your knowledge domain to verify appropriate uncertainty signaling rather than hallucinated responses.
5. Establish Continuous Monitoring in Production
Testing continues beyond deployment, with production data providing the most valuable insights on real-world performance.
- Shadow Mode & A/B Testing: Evaluate new prompts or RAG configurations through parallel processing or controlled experiments before full deployment, comparing key metrics.
- Drift Detection: Monitor retrieval quality trends and function call rejection rates to identify performance degradation from aging knowledge bases or embedding model issues.
- User Feedback Loop: Capture both explicit feedback (ratings) and implicit signals (query reformulations) to continuously refine testing datasets based on actual user challenges.
Are you ready to elevate the reliability and performance of your AI systems?
Our team of experts is here to provide tailored advice and strategies to ensure your AI applications are robust, safe, and effective. Whether you're refining your knowledge base, implementing automated safeguards, or enhancing your overall AI strategy, we can help you achieve excellence.
Reach out to us today for expert guidance and take the next step towards mastering your AI testing strategy.
