AI can sketch a working feature from a chat prompt in minutes. That speed is real — but so is the wall teams hit when they try to ship it: inconsistent output, missing tests, surprise security gaps, and pull requests nobody wants to own. The problem isn't the AI. It's the absence of a workflow.
This session compares three concrete approaches teams are using right now — Vibe Coding, Context Engineering, and Spec-Driven Development — with honest trade-offs for each. We'll demo vibe coding to show where exploration shines and where it collapses at review time. Then we'll layer in context engineering: plugging in your own docs, API schemas, and tool permissions to get consistent, bounded outputs. Finally, we go spec-first: define constraints and acceptance criteria up front, then let AI generate specs, tasks, and implementation in a reviewable, CI-anchored pipeline.
Every transition is shown live using a small feature built end-to-end, with templates you can bring back to your team on Monday.

Attendees leave able to:

- Differentiate vibe coding, context engineering, and spec-driven development by risk, speed, and governance
- Identify which approach fits exploration vs. delivery vs. production hardening
- Design a lightweight spec-driven workflow that produces reviewable PRs
- Apply context-engineering patterns to improve AI output consistency
- Evaluate trade-offs for existing codebases — not just greenfield