Free Webinar, Oct 27Stop your AI agents from writing flaky playwright tests.
Traditional software is designed to produce predictable results. LLM-powered applications aren't.
The same input can produce different outputs. A response can look convincing and still be factually wrong, irrelevant, unsafe, or inconsistent with your business rules. A change to the model, prompt, retrieval system, knowledge base, or guardrails can also create problems that a traditional test suite may never catch.
ThinkSys provides LLM testing and GenAI application testing services that combine functional QA, AI evaluation, adversarial testing, regression testing, and human review. We help teams find quality issues before their users do.
Functional QA alongside AI evaluation
Accuracy, groundedness, and hallucination checks
Adversarial and red-team testing
AI regression testing after every change
Human review for high-risk scenarios
Safety, policy, and business-rule coverage
ThinkSys tests LLM and generative AI applications for accuracy, groundedness, hallucination, safety, and task completion, not just whether the software runs. We build golden datasets, scoring rubrics, and adversarial test suites, then run repeatable evaluations across model, prompt, retrieval, and guardrail changes. Traditional functional QA runs alongside AI evaluation because an AI feature can pass every eval and still fail as a product.
Traditional software testing often starts with a simple question: did the system produce the expected result? LLM testing has to go further.
A useful AI response must do more than appear on the screen. It should be accurate. It should answer the actual question. It should use the right information. It should follow your rules. And it should remain reliable when users ask unexpected questions.
That wider view helps teams test the behavior of the AI application, not just the code around it.
Is the response accurate and grounded in the available information?
Is it relevant to what the user actually asked?
Does it follow system instructions, policies, and business rules?
Does it behave consistently across users, prompts, and repeated runs?
How does it respond to vague, conflicting, or adversarial requests?
Does a new model or prompt improve one area while hurting another?
Can an AI agent choose the right tools and finish the intended task?
A reliable LLM testing process needs repeatable evaluation, clear success criteria, and test scenarios based on how people will actually use the product.
Our LLM evaluation framework follows six practical steps.
We start with the application’s real use cases, users, business rules, and risks. Then we define measurable success criteria. Depending on the product, this may include factual accuracy, groundedness, relevance, tone, safety, policy compliance, or successful task completion.
We create evaluation data that reflects real usage. This can include golden datasets, expected responses, scoring rubrics, user personas, multi-turn conversations, edge cases, and adversarial scenarios. The goal is not to test only the happy path. It is to see how the application behaves when the input becomes harder.
We measure the dimensions that matter for your product. These may include accuracy, groundedness, hallucination, relevance, completeness, consistency, tone, safety, policy adherence, and task completion. We use deterministic checks where a clear correct answer exists. We use model-based grading and human review where quality requires judgment.
Real users do not always ask clean, predictable questions. We test ambiguous requests, conflicting context, unusual inputs, prompt injection attempts, jailbreak scenarios, and other risks that matter to your application.
A small prompt change can improve one behavior and quietly damage another. Our AI regression testing process helps teams compare results after changes to models, prompts, retrieval pipelines, knowledge bases, orchestration logic, tools, and guardrails. Repeatable evaluation makes it easier to see whether a release made the product better, worse, or simply different.
Failures become new test cases. As the application reaches more users, we can add real-world scenarios to the evaluation set and use them to improve prompts, product behavior, safeguards, and future releases.
Most engagements start with Steps 1 and 2 - agreeing what "good" means for your application, and building the first golden dataset against it. That's usually two to three weeks.
Good LLM evaluation metrics depend on what the application is expected to do. We select metrics based on the product, use case, level of risk, and type of response being generated.
| Dimension | What We Evaluate | Testing Method | Typical Signal |
|---|---|---|---|
| Accuracy | Is the response factually correct? | Golden datasets, deterministic checks, and model-based grading | Exact match or rubric score |
| Groundedness | Is the response supported by the retrieved information? | Evaluation against retrieved source context | Citation-support rate |
| Hallucination | Does the response make unsupported claims? | Adversarial prompts and groundedness checks | Unsupported-claim rate |
| Relevance | Does the response answer what the user asked? | Model-based grading and review | Relevance score |
| Completeness | Does the response include the information a good answer requires? | Expected-response rubrics | Rubric score |
| Consistency | Does quality remain stable across repeated runs and users? | Repeat runs and variance analysis | Output variance |
| Safety & Policy | Does the system follow safety rules and business policies? | Adversarial testing and rule-based checks | Policy violation rate |
| Task Completion | Does the agent finish the intended task correctly? | Scripted multi-step scenarios | Task success rate |
Is the response factually correct?
Golden datasets, deterministic checks, and model-based grading
Exact match or rubric score
Is the response supported by the retrieved information?
Evaluation against retrieved source context
Citation-support rate
Does the response make unsupported claims?
Adversarial prompts and groundedness checks
Unsupported-claim rate
Does the response answer what the user asked?
Model-based grading and review
Relevance score
Does the response include the information a good answer requires?
Expected-response rubrics
Rubric score
Does quality remain stable across repeated runs and users?
Repeat runs and variance analysis
Output variance
Does the system follow safety rules and business policies?
Adversarial testing and rule-based checks
Policy violation rate
Does the agent finish the intended task correctly?
Scripted multi-step scenarios
Task success rate
No single metric tells the full story. We combine signals where needed so teams can understand both output quality and product behavior.
LLMs rarely operate alone. They sit inside applications with interfaces, APIs, retrieval systems, tools, workflows, permissions, and business logic.
ThinkSys tests across that complete stack.
Our generative AI testing services assess the quality, reliability, and consistency of generated output across real-world scenarios. We test how the application responds to normal requests, edge cases, repeated prompts, changing context, and system updates.
A RAG application can retrieve the right document and still generate the wrong answer. Our RAG evaluation process tests whether the system retrieves useful information, uses that information correctly, stays grounded in the provided context, and avoids unsupported claims. We can evaluate retrieval quality and generated-answer quality separately, so your team can see where failures begin.
AI chatbot testing needs more than checking individual responses. We test multi-turn conversations, context retention, instruction following, relevance, tone, error handling, conversation flow, and task completion. We also check what happens when a user changes direction, refers to earlier information, provides incomplete details, or asks something outside the expected path.
AI agents introduce another layer of risk because they can take actions, call tools, and make decisions across several steps. Our AI agent testing and AI agent evaluation services assess whether an agent:
We test the final result and the path the agent took to get there.
Users experience the whole product, not just the model response. We combine AI evaluation with functional testing, integration testing, API testing, UI testing, automation, and end-to-end QA to check how AI-powered features behave inside the full SaaS product.
An application can work well for cooperative users and still fail when someone intentionally pushes its limits. Our LLM red teaming approach tests the application against risks that match its actual architecture and use case.
This can include:
Rather than treating AI red teaming as a generic checklist, we build scenarios around the data, tools, workflows, permissions, and risks within your application.
For broader application security requirements, explore our Security Testing Services.
Not every AI response can be scored in the same way. ThinkSys combines three forms of evaluation depending on what needs to be measured.
We use rule-based checks when an output has a clearly defined correct or incorrect result. This works well for structured values, formats, calculations, required fields, API outputs, and other predictable results.
We use LLM-based graders and scoring rubrics when an answer needs more nuanced assessment. This can help measure areas such as relevance, completeness, tone, groundedness, and instruction following at scale.
Some decisions still require human judgment. Experienced reviewers can assess subjective, ambiguous, high-risk, or user-experience-driven scenarios where automated scoring alone is not enough. The right evaluation program often uses all three.
A high model score does not guarantee a good product. The response may be accurate while the application around it fails. The page may break on mobile. A loading state may confuse users. An API error may erase conversation context. The AI may provide an answer that conflicts with the billing system. An agent may complete the right task for the wrong customer.
That is why ThinkSys combines traditional software QA with LLM-specific evaluation.
We test functional behavior, integrations, workflows, APIs, interfaces, error handling, automation, and AI output quality together. Your customers use the product. They do not use an evaluation score.
We test an LLM application on two levels. Functional QA confirms that the application works as software. We test interfaces, APIs, integrations, workflows, error handling, and other conventional product behavior.
AI evaluation checks whether the generated output is accurate, grounded, relevant, consistent, safe, and useful. ThinkSys can build golden datasets, scoring rubrics, adversarial scenarios, and repeatable evaluations that run again after model, prompt, retrieval, or application changes.
The right LLM evaluation metrics depend on the application. Common measures include accuracy, groundedness, hallucination rate, relevance, completeness, consistency, policy adherence, and task completion.
We use deterministic tests when there is a fixed correct result. Model-based evaluation and human review help assess open-ended responses that require judgment.
Traditional QA usually tests software against predictable expected behavior. AI applications can return different responses to the same input. A response can also be technically valid while still being inaccurate, irrelevant, unsafe, or poorly grounded.
AI testing therefore adds output evaluation, repeated runs, adversarial scenarios, scoring rubrics, and behavioral regression testing to conventional functional QA.
RAG evaluation checks both retrieval and generation. We test whether the system finds useful source information, whether the LLM uses that information correctly, and whether the final response stays grounded in the retrieved context.
We also look for unsupported claims, missing information, poor citations, irrelevant retrieval results, and changes in quality after updates to the knowledge base or retrieval pipeline.
AI agent testing focuses on both the final outcome and the steps taken to reach it. We create multi-step scenarios and evaluate whether the agent understands the request, selects the right tools, follows workflow rules, handles failures, makes appropriate decisions, and completes the intended task.
For higher-risk agents, we also test permissions, unsafe actions, adversarial inputs, and failure recovery.
LLM red teaming is adversarial testing designed to find ways an AI application can behave incorrectly or unsafely. Tests may include prompt injection, jailbreak attempts, conflicting instructions, refusal bypass, information leakage, unsafe tool use, and attacks through retrieved content.
The most useful red-team scenarios reflect the application’s actual users, data, workflows, integrations, and risks.
We create a repeatable evaluation suite and run it before and after each significant change. The suite can test accuracy, groundedness, relevance, safety, task completion, and other important behaviors across a fixed set of scenarios.
This form of AI regression testing helps identify cases where a new model, prompt, retrieval configuration, or guardrail fixes one issue but creates another.
The cost of LLM testing depends on the application, number of use cases, evaluation scope, level of risk, test-data requirements, automation needs, and the amount of human review involved.
Your LLM application needs more than a working API and a passing functional test suite. Test its answers. Test its behavior. Test its limits. And test what happens every time the model, prompt, data, or workflow changes.
