An introductory guide to LLM testing

Large language models (LLMs) are a class of AI systems trained on massive text datasets to understand and generate human-like language. Examples include GPT-4 and other transformer-based models that power chatbots, content generators, and more. LLM testing refers to the process of evaluating these models to ensure they work as intended, produce reliable outputs, and do so safely. In traditional software, testing verifies that code meets requirements. With LLMs, testing must also account for the model’s behavior on a wide range of inputs. In this post, we’ll explore how to start testing LLMs.

Types of LLM testing

Testing LLMs often involves several layers, each focusing on a different quality aspect of the model. Below are some common types of LLM testing and what they mean in this context:

Unit testing

In LLM terms, unit testing evaluates the smallest pieces of functionality, often a single prompt-response pair. The idea is to check an LLM’s response to a specific input against expectations. For example, if you prompt the model with a request to summarize a paragraph, does it respond correctly and coherently? Unit tests for LLMs typically assert basic criteria: Is the answer factually correct? Does it stay on topic? Is the tone appropriate? This level of testing helps isolate issues in particular capabilities. Unlike traditional software, you usually won’t have an exact expected output string, but you can define acceptable criteria or use similarity metrics to judge correctness.

Testing LLMs often involves several layers, each focusing on a different quality aspect of the model.

Functional testing

Functional testing evaluates the model’s performance on a broader task or end-to-end use case, combining multiple unit-level checks. Instead of one prompt, a functional test might involve a scenario or a set of inputs covering a feature. For instance, testing an LLM’s summarization feature might involve feeding it several documents and evaluating the quality of all the summaries it provides as a whole.

Regression testing

LLMs often evolve through parameter updates or by replacing the model with a newer version. Regression testing means re-running a fixed set of test cases on a new model version to ensure it hasn’t gotten worse on any previously solved cases. This is crucial because LLMs are non-deterministic and complex; a tweak meant to improve one behavior might inadvertently degrade another. A regression test suite for an LLM might include hundreds of saved prompts covering a spectrum of use cases. The goal is to catch breaking changes, such as a new version that starts failing prompts that it used to handle correctly.

Performance testing

Performance testing for LLMs focuses on the efficiency and scalability of the model rather than its content accuracy. Key metrics include latency (how quickly the model produces a response), throughput (how many requests it can handle in parallel), and computational cost (CPU/GPU usage, memory consumption, or if using an API, cost per request). For example, if you integrate an LLM into a web app, you might simulate dozens or hundreds of concurrent users to ensure the model’s response time stays within acceptable limits. Performance testing might reveal that a model’s response time spikes with long prompts, or that it handles 10 requests/second fine but chokes at 50/second. Using tools like load testing software (for instance, Tricentis NeoLoad for API performance testing) can help gather these insights.

Responsibility testing

Responsibility testing is unique to AI systems like LLMs. It ensures the model adheres to responsible AI principles and doesn’t produce outputs that are biased, toxic, or otherwise unethical. This type of testing has become critical as LLMs are deployed in customer-facing or high-stakes applications. Test scenarios for responsibility might include prompts that probe for inappropriate behavior. For example, asking the model questions that could trigger biased responses or instructions to produce disallowed content.

Responsibility testing ensures the model adheres to responsible AI principles and doesn’t produce outputs that are biased, toxic, or otherwise unethical.

Getting started with testing LLMs

Now that we’ve outlined the what of LLM testing, let’s talk about how to begin. Testing an LLM can feel daunting due to its probabilistic nature, but a structured approach goes a long way:

Methodology and planning

Begin with a test plan that identifies the key areas to validate. For example, if you’re deploying an AI assistant, your plan may include conversational accuracy, compliance with a style guide, performance under load, and safety checks for inappropriate content. Define clear acceptance criteria for each area. For instance, accuracy might be measured by the percentage of prompts answered correctly according to some ground truth or expert judgment. Unlike a traditional app, an LLM’s requirements might be fuzzier, especially when trying to translate those into testable criteria or examples.

Set up

Prepare your test environment and data. If possible, fix the random seed or generation settings for the LLM during tests to reduce output variability. Many LLM APIs allow setting a temperature parameter; using a low temperature can make the model’s output more consistent run-to-run, which simplifies testing. Next, gather test cases. For deterministic software, you write expected inputs and outputs, but for LLMs you may instead gather representative prompts and define expected criteria for the outputs. For example, a test case could be a user question and you expect the model’s answer to contain certain key facts and no disallowed content.

Execution

With everything ready, you can execute your tests. For initial testing or smaller models, this might be as straightforward as running through prompts manually in a playground and noting the responses. But for a systematic approach, you’ll want to automate execution. There are emerging frameworks specifically for LLM evaluation. For instance, libraries like DeepEval or LangChain’s evaluation modules allow you to define LLM test cases and even use other AI models to help judge outputs. If you already use a test automation solution such as Tricentis Tosca for your software, you could incorporate LLM calls into Tosca’s workflow (e.g., Tosca can trigger an API call to your model and verify the response meets certain rules).

Judging an AI’s free-form text output is not always black-and-white.

Evaluation of results

Once you have the outputs from your test runs, the next step is evaluating them. This is arguably the hardest part with LLMs, because judging an AI’s free-form text output is not always black-and-white. Start with the easy cases: any outright failures to comply with instructions or factual errors should be logged as defects. For example, if a unit test expected the model to list three items and it gave only two, that’s a clear fail. If you’re using a test management tool like Tricentis qTest, you can store the results of each test case, attach the model’s output, and mark it as pass/fail with comments. This provides traceability—you’ll have a record linking specific test prompts to outcomes and any requirement they relate to.

Challenges of testing LLMs

Testing LLMs comes with its own set of challenges that veteran software testers may not have encountered before. Let’s discuss some common pain points and how to address them.

Traditional software is deterministic. Given the same input, it produces the same output every time. LLMs, as I said before, can produce different answers to the exact same prompt on different runs. This non-determinism makes it hard to define a single expected output. As testers, we must account for variability. The challenge is balancing realistic conditions with the need for consistent tests. Non-determinism also means you might see a bug once and not again. So, logging and reproducibility is crucial. The goal should be to design tests and evaluation methods that are robust to minor variations in wording.

Additionally, evaluating LLM outputs often drifts into subjective territory. Unlike a binary pass/fail scenario, you may need to introduce scoring (like a 0-5 rating) or allow partial credit. One practical tip is to establish clear evaluation guidelines. For example, if testing a legal document summarizer LLM, define what constitutes a critical omission versus a minor one in a summary. That way, evaluators have a consistent rubric.

An important aspect to LLMs is that they’re prone to hallucinations, meaning they sometimes fabricate information that sounds plausible but is false. For example, an LLM might output a very good explanation for a medical question that is completely erroneous. Detecting such hallucinations can require domain experts or cross-checking against trusted data sources. For now, a combination of automated checks and human oversight is the pragmatic approach to test LLMs.

Testing LLMs is challenging because you’re often aiming at a moving and sometimes blurry target. Acknowledging these challenges is half the battle.

Testing LLMs is challenging because you’re often aiming at a moving and sometimes blurry target.

Best practices for testing LLMs

Given the above challenges, what are some best practices to effectively test LLMs? Here are a few recommendations:

Design prompts with care

In LLM applications, the prompt is the test. Craft your test prompts carefully to cover the range of scenarios your model may encounter. This includes not just the “happy path” but also edge cases and adversarial cases. For instance, if your LLM assistant is meant to answer general knowledge questions, include some deliberately confusing or nonsensical questions in your tests to see how it copes. Also, document the prompts you use in testing as part of your test specs. A best practice is to maintain a library of prompts for various categories. This library can be reused and expanded as your LLM or its usage evolves. Good prompt design in testing often mirrors good prompt design in usage: be explicit, provide context where needed, and consider what hidden assumptions the model might make.

Ensure wide scenario coverage

LLMs are generalists, so you need to test broadly. It’s easy to test the few scenarios you can think of, but the model will inevitably face something you didn’t anticipate. Mitigate this by expanding your scenario coverage. Use brainstorming or requirement analysis to list out all distinct use cases and user personas that might interact with the model. Then derive test prompts for each. Don’t forget negative scenarios, which are inputs where the correct behavior is to refuse or produce a safe error. For example, tests where you ask the model for disallowed content (like advice on something dangerous) should verify the model appropriately refuses. Remember, unlike a function in code that does one thing, an LLM can potentially do many things, so our tests must span that space as much as possible.

Maintain traceability

As you test, maintain strong traceability. This means keeping clear records of which prompts were tested, what the outcomes were, and how they map to requirements or risk categories. If a stakeholder asks, “Does the AI avoid political opinions?”, you should be able to show the test cases that cover that and their results. Tools like test management systems (e.g., Tricentis qTest as mentioned earlier) can be invaluable for this, as they allow linking tests to requirements and logging results over time. When a bug is found, document the exact input that caused it and save the output. This becomes a regression test for the future once the issue is fixed or mitigated.

As LLM technology evolves, those who incorporate strong testing and feedback loops will be the ones to harness its power responsibly.

Conclusion

In closing, the importance of testing LLMs cannot be overstated. It’s not just about catching bugs, but about testing AI behavior in a direction that aligns with user needs and ethical norms. When you’re building an LLM testing strategy, you’re empowering your team to innovate with AI confidently. So start small, perhaps with a few unit tests on your model, and iteratively expand your coverage. Learn from each failure and refine your approach. With each test run, you are essentially teaching and shaping the model’s role in your application. As LLM technology evolves, those who incorporate strong testing and feedback loops will be the ones to harness its power responsibly.

When you apply a mix of traditional QA processes with new AI-specific practices, we can train these models’ unpredictability. “‘Hallucinations’ are a critical problem… because users cannot trust that any given output is correct.” This quote from a 2024 Nature article encapsulates the stakes well: trust is hard-earned and easily lost with AI. Through diligent testing, we aim to build that trust.

This post was written by David Snatch. David is a cloud architect focused on implementing secure continuous delivery pipelines using Terraform, Kubernetes, and any other awesome tech that helps customers deliver results.