How we use LLMs across generation, accessibility review, and execution support
LLMs are most useful when they are placed inside a controlled QA workflow. The goal is not generic chat output, but better scenario shaping, clearer review context, and more reliable operator decisions.
Key Signals
Generation
Sharper
LLMs help shape scenarios from intent, not from scripts.
Review
Clearer
Outputs become easier for teams to interpret and act on.
Control
Required
Model choice matters when AI is part of product behavior.
Where LLMs create value in the QA workflow
Product Proof
01
Rafi Gen uses model reasoning to turn intent and documentation into cleaner scenario drafts, not just chat-style suggestions.
02
Rafi Accessibility Engine benefits when AI helps summarize review context and point teams toward the highest-priority issues first.
03
Rafi Run gains from better operator support during noisy release moments, while model selection remains a governed product decision.
LLMs need a controlled job inside QA
An LLM is not valuable just because it can generate text. In QA, it needs a controlled purpose: shaping scenarios, clarifying review context, or helping operators move faster with better information.
If the model is left as a generic assistant, the output becomes inconsistent and difficult to trust. The surrounding workflow is what turns LLM output into a usable product capability.
Where RafiRun applies LLM support
Rafi Gen uses LLM support to interpret intent and shape scenario drafts that match the business path the team actually wants to validate.
Rafi Accessibility Engine can use the same intelligence layer to make review output clearer and more actionable, while Rafi Run benefits from better execution context and more informed operator decisions during change-heavy flows.
Why model governance still matters
Because LLMs are now part of product behavior, teams need to control which model powers public or internal workflows. That is why model selection, cloud fit, and operational visibility matter so much.
The strongest result comes from combining model flexibility with a strict product workflow, so the team gains speed without losing trust or consistency.
Trial Workspace
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