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Using Devplan in Practice

· ·3 min read
Using Devplan in Practice

This walkthrough explains how Devplan is used in real day-to-day development. More than 90% of the code shipped runs through Devplan, making it foundational for fast execution and AI-enabled development benefits.

The goals are to create a repeatable, scalable system where AI can:

  • Get to a working solution independently
  • Execute tasks in parallel
  • Require minimal human oversight

Without Devplan, the overhead of managing AI workflows can cancel out benefits. With it, the advantages are tremendous.

1. Define Product & Technical Specs with Devplan Agents

Projects start with Devplan’s agents helping define requirements. They ask clarifying questions, flag ambiguity, and scope work properly—grounded in codebase knowledge, past projects, and company structure.

This step is critical because the quality of AI questions surfaces misalignments or assumptions that would cause failures or multiple follow-ups. By the end, you have a clean, scoped project with resolved ambiguity.

2. Break the Project Down into Right-Sized Features

Devplan automatically breaks each project into individual features or user stories, with one prompt per feature.

Your job is light validation:

  • Are features correctly sized (ideally half-day to 5-day chunks)?
  • Are there too many or too few?
  • Do acceptance criteria make sense?

Thanks to planning in Step 1, this typically takes less than two minutes.

3. Run Prompts into Your AI IDE (Manual vs. Devplan CLI)

Once features and prompts are ready, run them in your IDE of choice—Claude, Cursor, Junie, etc.

Approach 1: Manual Execution

Per feature:

  1. Download the generated prompt and format it for your IDE
  2. Clone your repository or create a new worktree
  3. Open your IDE manually in the correct folder
  4. Prompt the AI to begin coding

Doing this 6–10 times per day becomes tedious, repetitive, and error-prone.

Approach 2 (recommended): Automated Execution with Devplan CLI

With Devplan CLI, overhead disappears. Spin up a feature-ready workspace with one command:

devplan clone -c XX -p YYYY -y -i cursor -f ZZZZ

This one-liner:

  • Creates a scoped cloned folder for the feature
  • Launches your IDE in correct context
  • Automatically references the correct prompt file

Then tell your AI agent: “Implement current feature.”

Before the CLI, time and energy were lost getting into features and switching between terminal, prompts, and IDEs. Parallel execution felt clunky, and small errors led to broken states. With the CLI, feature execution is fast, consistent, and repeatable—making scale possible.

4. Review and Polish the Output

This is the last human step before shipping. The amount of work drops dramatically if planning and prompting were done well.

Once the AI has written code:

  • Manually review the output
  • Fix issues or edge cases
  • Test to ensure it meets standards

Without this system, far fewer AI-generated features could complete per day. Devplan turns isolated prompts into a real production workflow.

Devplan makes AI-assisted development planning 8–10x faster compared to manually managing specs, prompts, repos, and execution. Overall coding execution is 2-3x faster. More importantly, it makes the workflow scalable.

Requirements Adjustments

When an AI-coding agent goes sideways, it’s often easier to restart with corrected requirements. This workflow allows full restarts in minutes or seconds.

Go back to Step 1 and update the PRD or tech design doc. Then regenerate features and prompts with a single click in the Build Plan. Finally, use the CLI to restart with updated requirements—usually under 2 minutes total.

Centralizing requirements means every change persists, even if the repo is replaced or you switch AI IDEs. Changes in rule files won’t carry over to the next feature and may be lost if you switch tools.

Conclusion

Some articles suggest AI may be a net loss for productivity. Indeed, without smart usage or good tooling, this may be true. For professional engineers who are already efficient, minimizing overhead while empowering AI is critical. Every minute of overhead and context switch matters. Used well, AI can make engineers more productive and the job itself more enjoyable.