Spec-Driven Development with Coding Agents

A free DeepLearning.AI short course built with JetBrains and taught by Paul Everitt, a JetBrains developer advocate, running roughly an hour across fifteen video lessons plus a graded assignment. The premise is that vibe coding is fast but frequently produces code that does not match what you asked for, and that a written specification is the cheapest way to keep a coding agent aligned across a change larger than a single prompt. The lesson sequence is a complete workflow rather than a set of tips: Why spec-driven development, Workflow overview, Set up your environment, Creating the constitution, Feature specification, Feature implementation, Feature validation, Project replanning, The second feature phase, The MVP, Legacy support, Build your own workflow, and Agent replaceability. You begin by writing a project constitution — mission, tech stack and roadmap — as a durable document the agent re-reads, then run repeated plan-implement-validate loops for each feature, deliberately replan mid-project when reality diverges from the spec, and drive through to an MVP. Two later lessons matter more than the running example: applying the same discipline to an existing legacy codebase, and packaging your workflow as an agent skill so it is portable across agents and IDEs rather than tied to one vendor's tool. The wider ecosystem context is GitHub's open-source spec-kit toolkit, which popularised this constitution-then-specification pattern.

Topic

Agentic

Level

Beginner

Format

Course

Cost

Free

Duration

~1 hour, 15 video lessons plus a graded assignment, self-paced

Provider

DeepLearning.AI

Hands-on

Yes — code/exercises

Certificate

Available

• ✓Contrast vibe coding with spec-driven development and when each one works
• ✓Write a project constitution covering mission, tech stack and roadmap
• ✓Run repeatable plan-implement-validate loops for each new feature
• ✓Replan a project when implementation diverges from the original specification
• ✓Apply a spec-driven workflow to an existing legacy codebase
• ✓Package your workflow as a reusable, portable agent skill
• ✓Keep a workflow agent-agnostic so the underlying tool stays replaceable

• •Taught by Paul Everitt of JetBrains, a developer advocate with a long track record in Python tooling education
• •Covers legacy codebases, which is where spec-driven advice usually stops and real work starts
• •Ends on agent replaceability — the workflow is portable across agents and IDEs, not locked to one vendor
• •About an hour long, so the time cost is smaller than one botched vibe-coding session
• •Complements GitHub's spec-kit, the open-source toolkit that popularised the constitution-then-spec pattern

This page was written from 3 sources, 2 on domains other than deeplearning.ai.

1. 1.deeplearning.ai — spec driven development with coding agentsvendor
2. 2.github.com — spec kit
3. 3.blog.jetbrains.com — jetbrains academy april 2026