AI can sketch a working feature from a conversational prompt. That vibe-coding style is fast and useful for exploration, but it becomes risky when the result must survive security review, changing requirements, and a team that did not share the original conversation.

This session builds the same feature three ways. First, we use vibe coding for a quick spike. Next, we add context engineering: repository instructions, domain documentation, API schemas, examples, and tool permissions. Finally, we make the work spec-driven with explicit contracts, acceptance criteria, tasks, tests, and CI gates. The comparison shows where each workflow saves time, where it creates hidden work, and when moving to the next level is worth the cost.

Attendees will leave able to choose a workflow based on risk and lifecycle, design useful context for an AI coding tool, and create a lightweight specification that produces reviewable code instead of an impressive but ownerless demo.

Audience: Developers and engineering leads adopting AI coding workflows.
Format: 45–60-minute technical talk with one feature implemented three ways.
Demo: Vibe-coded spike → context-engineered revision → spec-driven pull request with tests and CI checks.
Take-home artifact: Workflow decision matrix and specification/checklist templates; talk-specific package is not yet published.
Evidence: Submitted to six conferences; prior NDC Sydney submission was withdrawn.
Vendor scope: Vendor-neutral workflow; examples can be demonstrated with common AI coding assistants.