Gen AI & LLMs

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 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.