Faster test case creation with AI and LLMs
- Use multiple models for idea generation on test cases for safer parts of the app.
- Tune prompts and guardrails for product context and naming.
- Keep review in the loop for final acceptance.
Teams want faster cycle time and better coverage, yet privacy and compliance matter. SequoiaAT uses careful AI setups to raise speed while keeping control of code and data.
Client stories
AI created data for automation at scale
- Plug a large language model into the test farm to supply fresh inputs on demand.
- Include rare and boundary inputs to stress the system.
- Track pass and fail trends to guide next runs.
Triage insights that improve coverage
- Use ML to compare triage notes with the test library and find gaps.
- Suggest new cases that mirror real issues seen in the wild.
- Fold results into the plan for the next sprint.
Pilot of offline AI for unit tests
- Run an offline model like Llama inside the build setup.
- Limit scope to selected repos and keep developer review in place.
- Measure speed, flakiness, and real fault catch rate before scale up.