Dev Plans Turn Fast Models Into Reliable Builders

Grok Coder Fast ran for hours and produced usable code.

The expensive model spiraled after ten minutes.

The difference wasn't the model. It was the plan.

The spiral without a map

You open a new task. You describe the feature you want: "add user authentication with OAuth." The model starts generating. Files appear. Functions get written. Imports pile up.

Forty minutes later, you realize the model has built its own auth framework instead of using the library you already have. It's generating code that conflicts with your existing user table. It's solving problems you didn't ask it to solve.

You intervene. You clarify. The model course-corrects, but now it's working against its own earlier output. The context window fills up with false starts. The cost meter runs.

This is what happens when you prompt without a plan. The model is executing, but it's executing without constraints. Every decision it makes is a guess about what you meant.

The dev plan as specification

A dev plan is not a vague description. It's a specification: each step includes exact instructions about what to accomplish, which files to touch, which patterns to follow.

When each step is explicit, even inexpensive models can run autonomously for hours. The model isn't guessing about scope. It's checking steps off a list.

"Building a dev plan is the difference between fumbling around blindly not knowing what you're doing and fumbling around with glasses on not knowing what you're doing because it yeah you get much better results."

Ru Tang, After Hours S01E02

The glasses metaphor is apt. The model still doesn't "understand" your codebase the way you do. But with a plan, it knows where to look and what to produce. The fumbling becomes directed fumbling.

How plans enable orchestration

When you give a dev plan to orchestrator mode, it doesn't just run the steps sequentially. It passes the plan to architect mode or ask mode to figure out the details: which tasks go to which mode, what order makes sense, where dependencies exist.

The plan becomes a coordination document. Instead of one model doing everything (and drifting), specialized modes handle their parts. The plan keeps them aligned.

"The only reason I was able to do that is because I gave it a really complete and complex dev plan."

Ru Tang, After Hours S01E02

"Complete and complex" is the operative phrase. A plan that says "add auth" is not a plan. A plan that says "Step 1: Create migration for sessions table with columns X, Y, Z. Step 2: Add session middleware in /src/middleware/auth.ts following the pattern in /src/middleware/logging.ts" is a plan.

The tradeoff: upfront time

Writing a dev plan takes time. If you're doing a five-minute fix, a detailed plan is overhead.

But for any task that takes longer than your attention span, the math favors planning. Teams that skip planning spend more time fixing mid-task derailments than they would have spent writing the plan. The derailment isn't just lost model output; it's lost context, lost focus, and often a restart from scratch.

The threshold is roughly this: if you're going to let the model run for more than ten minutes unsupervised, write a plan first.

Why this matters for your team

For a team of five shipping production features weekly, the difference between "model that drifts" and "model that follows a plan" compounds across every task. If two features per week hit the spiral problem, that's hours of cleanup, context-switching, and re-prompting. Hours that could have been one planning session upfront.

The shift is not "use a smarter model." The shift is "give any model a smarter input."

How Roo Code closes the loop with dev plans

Dev plans work because they turn vague intent into executable specification. Roo Code amplifies this pattern through orchestrator mode, which coordinates specialized modes to execute each step of your plan while closing the loop on the results.

With BYOK (bring your own key), you control model costs directly. A detailed dev plan means you can route simple steps to fast, inexpensive models and reserve expensive models for judgment calls. The agent runs tests, checks outputs against your plan, and iterates when steps fail. You spend tokens intentionally for outcomes, not for drift correction.

Citable insight: Dev plans transform any AI model into a reliable builder by replacing guesswork with explicit step-by-step specifications that orchestrator mode can coordinate across specialized agents.

Comparing approaches: prompts vs. dev plans

The first step

Before your next multi-step task, write the plan first. Make each step specific: which files, which patterns, which outcomes. Give the model constraints instead of intentions.

The model that follows a plan looks like a reliable builder. The model without one looks like expensive slop.