Most GenAI implementations in SDLC are probabilistic by design.
That works for brainstorming — it fails in enterprise delivery.
In real programs, SDLC requires:
- Consistent outputs across runs
- Traceability from requirements → design → code → tests
- Audit‑ready decisions
- Repeatable QE coverage (functional + non‑functional)
Randomness helps creativity.
SDLC needs determinism.
We’re seeing a shift from “AI assistants” to deterministic intelligence embedded directly into SDLC workflows.
How are teams balancing speed vs determinism in AI‑led engineering?
These patterns are something we’re implementing in deterministic SDLC platforms like SASVA 4.0.
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