Why traditional testing methods fail with LLMs
LLMs are reshaping the enterprise tech landscape, but they also introduce a host of new testing challenges that traditional QA practices simply weren’t built for. As black-box, probabilistic engines, LLMs behave unpredictably, evolve constantly and are increasingly subject to strict compliance scrutiny. As enterprises bet their future on LLMs, the need for robust, transparent and repeatable testing is now mission-critical.
Here are four of the biggest barriers to effective LLM testing:
Unobservable, probabilistic behaviour
Unlike deterministic code, LLMs can produce different outputs from the same input, depending on subtle contextual factors or model drift. This makes it extremely difficult to validate behaviour, catch regressions, or ensure reliability without robust black box testing.
Complex, multi-part processes
LLMs are part of a complex process involving: prompt engineering, model context protocols and input data. Testing just the final output isn’t enough, all the inputs and their dependencies must be understood and matched with a clearly defined expected result.
Data dependency and instability
LLMs are highly sensitive to input data quality and structure. Prompts, fine-tuning data, knowledge sources and user inputs can all affect results in unexpected ways. This makes bug reproduction difficult and demands deep control over test data.
Regulatory and reputational risk
As LLMs are deployed into critical functions, mistakes can lead to serious consequences, from legal exposure to brand damage. With regulations like GDPR, the EU AI Act and DORA tightening, enterprises must prove their AI systems are safe, reliable and accountable.
A systemic framework for testing LLMs
To meet these challenges, organisations need more than patchwork testing, they need a structured, systemic framework designed specifically for LLM behaviour. This webinar showcases how systemic, robust testing can help you deliver future-proof LLMs.
We explore a systematic approach to LLM testing, built around five essential pillars:
Component-level isolation and control:
LLM systems are built from multiple components: prompts, knowledge retrieval, and learned models. Testing requires the ability to isolate each part, freeze and reuse inputs and vary components independently. This makes debugging, regression testing and version control feasible, even within complex AI systems.
Data-driven, risk-focused testing:
Real-world data is messy, evolving and not all inputs are well-formed. LLM testing must go far beyond ideal scenarios, using diverse and intentionally broken inputs to explore model weaknesses. Synthetic data plays a crucial role, allowing teams to simulate a wide range of test conditions while avoiding production data risk.
Compliance and future-proofing:
With increasing regulatory scrutiny, testing frameworks must provide full traceability and accountability. That means tracking model behaviour over time, maintaining version-aware test records and ensuring outputs remain within safe, ethical and legal boundaries, even as your models evolve.
Machine learning models:
LLMs are powered by complex statistical algorithms, and testing them requires the ability to simulate large volumes of statistically varied data. This data must reflect real-world patterns while also being extended through techniques like time series generation and multivariate dependency modelling. These distributions help future-proof models by enabling predictions of unseen user behaviour and supporting scenarios where historical data is lacking.
Automated, intelligent output validation:
LLM outputs often vary in structure and phrasing, making strict binary assertions ineffective. Instead, intelligent validation, such as fuzzy matching and semantic comparison, is needed to assess accuracy. These techniques account for the flexible and probabilistic nature of language generation. Scalable automation enables this process, allowing teams to monitor output quality and detect regressions across prompts, model versions, and software changes.
Meet our speakers, AI testing experts:
Whether you're developing LLMs in-house or integrating them into business-critical workflows, this webinar will equip you with the tools and mindset to build more reliable, scalable and future-ready LLMs.
Huw Price - Chairman & Co-Founder
Described as “The Godfather of test data”, Huw is a serial test data inventor, and has worked with the world’s largest organisations to transform their test data management. With Curiosity Software, Huw leads on crafting innovative solutions to drive test data and quality success for our enterprise customers.
Harry Burn - Head of Solution Engineering
Harry is a test data management and visual modelling specialist. Since 2020, Harry has worked with some of the largest global organisations across finance, banking, healthcare and manufacturing, helping them to solve complex data generation, compliance and quality challenges.
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