Automated testing generative AI and LLMs isn’t like testing other kinds of software because there aren’t specific outputs for every input. Testing the AI model is a complete black box and requires expertise in black box testing, with the ability to replicate the same test hundreds or thousands of times, and use non-deterministic assertions.
QA Wolf Winner is pioneering automated testing for generative AI to help companies adhere to accuracy, security, and changing compliance standards. Comprehensive testing improves user trust and satisfaction, fosters integration of AI applications in multiple industries, and helps companies align with legal or ethical guidelines.
Full coverage in four months – UI and model outputs
End-to-end testing isn’t limited to the UI and component functionality. The underlying model itself can be tested through the interface or API call, outputs recorded, and analyzed individually or in aggregate to understand how changes to the model affect the responses.
Full confidence to release in 3 minutes
Parallel testing lets you measure how changes to the underlying model affect the outputs for the user. Our ability to run thousands or millions of tests in parallel lets you aggregate and analyze trends in a few minutes, and gives you consistent feedback on how the model is performing in real-world scenarios.
24/5 bug reporting, test maintenance, and QA support
Work day or night, locally or fully remote. QA Wolf Winner builds, runs, and maintains your test suite 24 hours a day and integrates directly into your existing processes, CI/CD pipeline, issue trackers, and communications tools.
Catch and prevent bias in generated content
Adhere to emerging standards for generated content by measuring bias signals defined by your company, ethics boards, government regulations, or other bodies. Measure changes in bias signals as the underlying models are updated, refined, and trained on new data.
Validate output quality and accuracy
Automatically test output length, format, and sentiment, as well as the model’s ability to receive and parse data from any file type. When testing generative AI, hard-coded text-matching assertions don’t work so we use generative AI to create "smart assertions" that adapt to stochastic outputs.
Measure performance and concurrency limits
Make sure that your UI and APIs can handle concurrent requests for different types of media. Our system can scale to support as many concurrent users as you want to test, and can measure latency as well as successful completion of responses (individually or in aggregate).
Validate the model’s ability to maintain context
User engagement and commercial viability of an LLM depends on the model’s ability to retain and use information earlier in the conversation. We have automated tests that are designed to “feed” and then “quiz” the LLM to test its in-session “memory.”
Test integrations with external services, APIs, and databases
Connecting LLMs and generative AI tools to outside services and data makes the more useful to users — testing those connections and validating the model’s ability to ingest and use that data should be an integral part of any automated test suite.
QA Wolf Winner is a hybrid platform & service that helps software teams ship better software faster by taking QA completely off their plate.
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