Testing large language models in production

Consider a customer support chatbot powered by an LLM. It works well in testing, giving accurate and helpful responses. But in production, users ask questions in different ways, provide incomplete context, or mix multiple intents into a single query. LLMs are trained on large datasets in controlled environments, but real-world inputs are unpredictable.

The model may misunderstand the request or generate incorrect answers. This can lead to issues like hallucinations, inconsistent responses, and a poor user experience.

For customer-facing applications, this can reduce trust, increase support costs, and impact business outcomes.  

In our experience at GAT, we have observed that these issues often stem from gaps in pre-production testing, as it cannot fully simulate real user behavior, environments, or edge cases at scale. To solve this, teams need strong LLM production testing and monitoring.

This article explains how to test LLMs in production. It covers key challenges and strategies to help teams build reliable and safe Generative AI systems. 

Why testing LLMs in production is different

We often observe LLMs behaving unpredictably when exposed to real users. This is not just unpredictability; it comes from three key factors.

• Dynamic inputs (unstructured, free-form queries from real users) vary far more than pre-production datasets can cover. In production, users phrase questions in many different ways. They may use slang, mix languages, or provide incomplete context.
• Context drift can affect how models respond over time. In longer conversations or repeated interactions, the model can lose track of context or produce inconsistent answers.
• System integration also adds complexity. LLMs often connect to APIs, databases, or third-party tools. Even small changes in these systems can lead to unexpected failures or incorrect outputs.

Unlike traditional software, where regression testing ensures stability across releases, LLM behavior is non-deterministic. Outputs can change without code updates, making standard QA approaches insufficient.

This directly impacts business outcomes, e.g., inaccurate responses, latency spikes, or failures can lead to poor user experience, lost revenue, or reputational damage.

To handle LLMs’ non-deterministic behavior, teams need scalable, diverse, and adversarial real-world testing. Our crowdtesting across thousands of devices can reveal edge cases, such as inconsistent responses or localization gaps, that pre-production tests often miss. This approach helps ensure models align with safety and brand guidelines across diverse users and environments.

However, these real-world conditions introduce a distinct set of challenges when testing LLMs in production.

Top 5 challenges of LLM testing in production

In our client projects, we consistently encounter recurring challenges when LLMs move into production environments. These challenges include:

LLM testing challenges in production

1. Non-determinism and hallucinations: LLMs can generate different responses for the same prompt, especially under real user load. This makes regression testing difficult and increases the risk of inconsistent user experiences.
2. Context drift, prompt injection, and bias amplification: Over time, models may lose context in multi-turn conversations or become vulnerable to malicious inputs. In live traffic, this can lead to unsafe or biased outputs that impact trust and even expose sensitive user data.
3. Scalability and performance issues: Latency spikes, token limits, and high inference costs often surface only at scale. These directly affect uptime, response times, and operational budgets.
4. Localization and accessibility gaps: Global users introduce language nuances, cultural context, and accessibility needs that are hard to simulate in pre-production testing.
5. UX and interaction quality: Even when responses are technically correct, poor phrasing or flow can break the user experience.

For instance, when Booking.com leveraged GAT to identify critical bugs across multiple markets, it emphasized that real-world testing can catch issues traditional pre-production checks might miss, a lesson vital for LLM monitoring.

Testing strategies for LLMs in production

Effective LLM testing in production requires a blend of automated monitoring and real-world validation. Our team at Global App Testing consistently applies this blend across global applications.

Below are the most effective strategies teams use to keep LLMs reliable in production.

LLM testing strategies

• Continuous monitoring: Teams need to track how models perform in real time. This includes response quality, consistency, latency, and even cost per interaction. Small drops in quality can quickly scale into major issues if not detected early.
• Canary deployments: Teams release changes to a small group of users first. This lets them observe the model in real conditions and fix issues before a full rollout. It may take more time, but it reduces risk by limiting the impact of any failures.
• Synthetic and real-world testing: To go deeper, teams combine synthetic and real-world testing. While simulated prompts help validate expected behaviors, GAT’s real device testing brings in actual users interacting with LLMs across devices, networks, and regions, surfacing issues that lab environments miss.
• Cost per interaction and token usage: Teams should track token usage per request, cost per interaction, and total spend over time. Setting limits on prompt size and response length helps control costs.
• A/B testing and shadow deployments: A/B testing allows teams to compare different prompts or model versions using live traffic. Shadow deployments go a step further by running updates in the background without affecting users, making it easier to evaluate changes safely. They help teams improve accuracy and user experience without risking all users at once.
• Error logging and alerting: Log all outputs and set alerts for failures, unsafe responses, or performance drops. This enables faster issue detection and resolution.
• Versioning and prompt testing: LLM behavior can change with small prompt updates. Maintaining version control for prompts and models allows teams to track changes and test variations. In practice, this helps avoid sudden drops in response quality after prompt tweaks.
• Localization and crowd testing: Two often overlooked strategies are localization testing and crowd testing. GAT’s global tester network enables validation of LLM outputs across languages, cultural contexts, and accessibility needs, ensuring responses resonate with diverse audiences.
• Data privacy and security testing: Teams must ensure that models do not expose sensitive user data. Test for prompt injections and data leaks. At GAT, we simulate real-world and harmful inputs to identify risks early and ensure outputs remain secure and compliant in production.
• Failure handling and fallback strategies: Systems should have fallbacks like safe responses or escalation to human support when the model is uncertain. For example, if a model gives an incorrect answer, the system can fall back to a safe response or a human agent to maintain user trust.

From an observability standpoint, teams should prioritize traceability of prompts and outputs, user feedback loops, and anomaly detection.

Key metrics to monitor LLMs in production

To test LLMs in production, teams need clear and simple metrics. These metrics should show both model quality and user impact.

From our experience at Global App Testing, the most useful metrics are simple and tied to real-world behavior. Such as:

Metrics to monitor LLMs in production

• Accuracy and relevance: Measure accuracy and relevance of responses through metrics like faithfulness (how well responses align with source data), semantic consistency across prompts (ensuring similar inputs produce logically consistent outputs), and attribution gap (highlights when responses lack clear support).
• Latency and response time: Track latency and response time, as they directly affect the user experience. Slow responses reduce engagement and increase drop-offs, especially in customer-facing applications. Monitoring response times under real traffic can help maintain performance at scale.
• Cost per interaction: Track tokens per request, cost spikes, and budget thresholds. LLM costs can scale quickly without proper monitoring.
• Safety and toxicity: Monitor outputs for harmful, biased, or unsafe content. This is important in live environments where inputs are unpredictable. Our team ensures testing across 190+ countries and 160+ languages to spot content bias or cultural mismatches.
• Logging and alerting: Log all prompts and responses. Use alerts to detect sudden drops in quality or spikes in errors. This helps to act quickly and run regression testing when needed.
• Localization and accessibility success rate: Test how the model performs across regions and devices. In one case, GAT helped Carry1st improve its completion rates by 12% by identifying issues in real user flows. In LLM testing, tracking consistency and accuracy across different inputs gives similar insights.

These metrics give teams a clear view of model behavior in production and enable faster, data-driven improvements.

Tools and frameworks for production LLM testing

In practice, no single tool can cover all aspects of LLM testing in production. We at GAT take a layered approach, combining observability platforms with real-world validation.

Ensure your GenAI performs flawlessly with GAT

Ensuring LLM quality in production requires both technical monitoring and real-world validation. Global App Testing supports this through services such as GenAI testing, crowdtesting, localization testing, UX testing, and real-device testing across global environments.

Our approach focuses on extending in-house QA with access to diverse environments, devices, and user demographics, enabling teams to test scenarios that are difficult to replicate internally.

If you are working with LLMs in production, speak with us for LLM testing to strengthen monitoring, reduce risk, and ensure your GenAI systems perform reliably in the real world.