AI can turn a conversational prompt into a working feature. That is useful when you are testing an idea. It becomes risky when the code must survive security review, changing requirements, and a team that never saw the original conversation.

The problem is not vibe coding itself. The problem is using an exploration workflow for production work without recognizing where the workflow needs to change.

This session builds the same feature three ways. First, we use vibe coding to create a quick spike. Next, we add context engineering: repository instructions, domain documentation, API schemas, examples, and clear tool permissions. Finally, we make the work spec-driven with explicit contracts, acceptance criteria, implementation tasks, tests, and CI gates.

We will compare where each workflow saves time, what hidden work it creates, and when the additional structure is worth the cost. The result is not a ranking—it is a practical way to match the workflow to the risk and expected lifetime of the code.

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

Not every task needs a specification. Every production change needs enough shared context for the team to own it.

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.
Vendor scope: Vendor-neutral workflow; examples can be demonstrated with common AI coding assistants.