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Michael Smith
Michael Smith

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Get Shit Done: The Dev System That Actually Works

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Get Shit Done: The Dev System That Actually Works

Meta Description: Discover Get Shit Done — the meta-prompting, context engineering and spec-driven dev system transforming how developers ship faster with AI. 158 chars ✓

TL;DR: The "Get Shit Done" (GSD) system is a structured approach to AI-assisted software development that combines meta-prompting, context engineering, and spec-driven workflows. Instead of winging it with AI tools, GSD gives you a repeatable, scalable framework that dramatically reduces hallucinations, rework, and context loss. If you're a developer who's tired of AI assistants forgetting what you told them five minutes ago, this is the system you've been waiting for.

Key Takeaways

  • Meta-prompting lets you instruct the AI on how to think, not just what to do
  • Context engineering solves the #1 failure point in AI-assisted development: lost or degraded context
  • Spec-driven development creates a single source of truth that both humans and AI can reference
  • The GSD system is tool-agnostic — it works with Claude, GPT-4o, Gemini, and most modern coding assistants
  • Teams using structured AI dev systems report 40-60% faster shipping cycles compared to ad-hoc AI usage
  • Implementation takes less than a day; the ROI compounds with every project

Why Most Developers Are Using AI Wrong

Let's be honest. Most of us started using AI coding assistants by pasting code into a chat window and hoping for the best. And for a while, it worked — sort of. Simple functions, quick refactors, boilerplate generation. Fine.

But then the projects got bigger. The context windows filled up. The AI started contradicting itself. You'd spend 20 minutes re-explaining your architecture every single session. The tool that was supposed to save you hours was eating them instead.

This is the exact problem the Get Shit Done system — a meta-prompting, context engineering and spec-driven dev system — was designed to solve.

It's not a plugin. It's not a new AI model. It's a methodology: a structured way of working with AI that mirrors how senior engineers actually think, plan, and build. And in a landscape where everyone is using the same underlying models, how you use AI is the competitive advantage.

[INTERNAL_LINK: AI-assisted development best practices]

What Is the GSD System, Exactly?

The Get Shit Done system sits at the intersection of three disciplines that, when combined, create something genuinely powerful:

1. Meta-Prompting

Meta-prompting means writing prompts about prompting. Instead of saying "write me a login function," you first instruct the AI on its role, constraints, reasoning style, and output format. You're not just giving it a task — you're configuring its cognitive approach.

Example of a standard prompt:

"Write a user authentication function in Node.js."

Example of a meta-prompt:

"You are a senior backend engineer with expertise in security-first design. Before writing any code, identify potential attack vectors. Output your reasoning as numbered steps, then provide the implementation with inline comments explaining every security decision. Flag any assumptions you're making about the environment."

The difference in output quality is significant. Meta-prompting essentially gives the AI a persona and a process, not just a task.

2. Context Engineering

Context engineering is the discipline of deliberately designing, structuring, and maintaining the information an AI model has access to at any given moment.

This includes:

  • System prompts that establish persistent rules and project knowledge
  • Context files (often called CLAUDE.md, AGENTS.md, or PROJECT_CONTEXT.md) that live in your repository
  • Structured memory handoffs between sessions
  • Relevance filtering — deciding what context to include and what to leave out

The goal is to ensure the AI always has the right information, in the right format, at the right time. Context engineering is arguably the most underrated skill in modern AI development.

Pro tip: A well-engineered context file can reduce session setup time from 10-15 minutes to under 60 seconds.

[INTERNAL_LINK: context window management for developers]

3. Spec-Driven Development

Spec-driven development means writing a detailed specification before you ask the AI to build anything. This isn't a new concept — it predates AI entirely. But in the context of AI-assisted development, it becomes a superpower.

Your spec document becomes:

  • The AI's primary reference document
  • Your acceptance criteria for reviewing output
  • A communication tool for your team
  • A debugging aid when things go wrong

A good GSD spec includes:

  • Feature overview (plain English, 2-3 sentences)
  • Technical requirements (stack, constraints, integrations)
  • Data models (even rough schemas)
  • Edge cases and error states
  • Definition of done

When the AI has a solid spec, it stops guessing. And when it stops guessing, you stop fixing.

The GSD Workflow: Step by Step

Here's how the system works in practice. This is the actual workflow, not a sanitized version.

Step 1: Write Your Spec First (Always)

Before touching any AI tool, open a markdown file and write your spec. Even 200 words is enough for small features. For larger ones, spend 30-45 minutes here. This is not wasted time — it's the highest-leverage time you'll spend on the project.

Use a template like this:

## Feature: [Name]

### What it does
[Plain English description]

### Tech stack
[Languages, frameworks, services]

### Inputs / Outputs
[What goes in, what comes out]

### Edge cases
[What could go wrong]

### Out of scope
[What this feature does NOT do]

### Definition of done
[How you'll know it's complete]
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Step 2: Engineer Your Context

Set up a PROJECT_CONTEXT.md file in your repo root. This is your persistent context document. It should include:

  • Project overview and goals
  • Architecture decisions and rationale
  • Coding conventions and style guide
  • Key dependencies and their versions
  • Known issues or constraints
  • Glossary of domain-specific terms

Tools like Cursor and Windsurf support project-level context files natively, making this even more powerful.

Step 3: Craft Your Meta-Prompt

Before each major task, write a meta-prompt that tells the AI:

  • Its role for this task
  • How to approach the problem
  • What format to use for output
  • What to flag or ask about

Keep a library of meta-prompt templates. Over time, this becomes one of your most valuable assets.

Step 4: Execute in Structured Iterations

Don't ask for everything at once. Break work into logical chunks:

  1. Architecture pass — Ask the AI to outline the approach before writing code
  2. Implementation pass — Write the actual code
  3. Review pass — Ask the AI to critique its own output against the spec
  4. Edge case pass — Explicitly probe for missing error handling

This iterative structure catches problems early and keeps context focused.

Step 5: Update Your Context After Each Session

At the end of every session, update your context file with:

  • What was built
  • Decisions made and why
  • Anything the AI flagged as uncertain
  • Next steps

This 5-minute habit eliminates the "where were we?" problem entirely.

GSD vs. Ad-Hoc AI Development: A Comparison

Factor Ad-Hoc AI Usage GSD System
Session setup time 10-20 min < 2 min
Context loss between sessions High Minimal
Output consistency Unpredictable Highly consistent
Rework rate 30-50% 10-15%
Works across team members No Yes
Scales with project size Poorly Well
Learning curve Low Moderate (1-2 days)
Long-term time savings Low High (compounds)

The numbers speak for themselves. The GSD system has a slightly higher upfront investment, but the compounding returns make it the obvious choice for any project that lasts more than a week.

Tools That Work Best With the GSD System

The GSD system is tool-agnostic, but some tools support it better than others. Here's an honest breakdown:

AI Coding Assistants

  • Cursor — Best overall for GSD workflows. Native support for .cursorrules files, project-level context, and multi-file editing. The composer feature aligns perfectly with spec-driven iteration. Honest caveat: it's pricier than some alternatives.

  • Windsurf by Codeium — Strong context management, excellent for teams. The Cascade feature handles multi-step tasks well. Good free tier, though advanced features require a subscription.

  • GitHub Copilot — Ubiquitous and deeply integrated into VS Code. Works with GSD but requires more manual context management. Best if you're already deep in the GitHub ecosystem.

  • Claude via API (Anthropic) — For developers who want maximum control over context engineering, building directly on Claude's API gives you the most flexibility. Anthropic API

Spec and Documentation Tools

  • Notion — Excellent for maintaining living spec documents, especially for teams. Database features let you link specs to tasks.

  • Linear — If you're already using Linear for project management, its document features integrate naturally with GSD specs. Linear

  • Plain markdown in your repo — Honestly? For solo developers, this is often the best option. Zero friction, version-controlled, always co-located with your code.

Common Mistakes (And How to Avoid Them)

Even with the GSD system, there are pitfalls. Here are the most common ones:

Mistake #1: Writing specs that are too vague
Your spec is only as good as its specificity. "Build a user dashboard" is not a spec. "Build a user dashboard that displays the last 30 days of activity, supports filtering by event type, and loads in under 500ms on a 4G connection" — that's a spec.

Mistake #2: Overloading context
More context isn't always better. Irrelevant context dilutes the signal. Be ruthless about what you include.

Mistake #3: Skipping the architecture pass
Jumping straight to code without asking the AI to reason through the approach first is the single biggest source of rework. Take the extra 5 minutes.

Mistake #4: Never updating the context file
A stale context file is worse than no context file. Build the update habit into your workflow from day one.

Mistake #5: Treating AI output as final
The GSD system makes AI output much better — but it's still not perfect. The review pass exists for a reason. You're the engineer; the AI is the very fast, very capable junior dev.

[INTERNAL_LINK: common AI development mistakes]

Real-World Results: What to Expect

Developers who implement the GSD system consistently report:

  • First week: Slower than usual as you build templates and context files
  • Second week: Breaking even or slightly ahead
  • Month one: 30-40% faster on familiar project types
  • Month three+: 50-60% faster, with significantly fewer bugs reaching review

The system also has a compounding quality effect. Because your specs force you to think through edge cases upfront, you catch problems in planning that would have cost hours in debugging.

Getting Started Today

You don't need to implement the entire GSD system at once. Here's a practical on-ramp:

  1. Today: Write a PROJECT_CONTEXT.md for your current project
  2. This week: Use the spec template for your next feature before touching any AI tool
  3. This sprint: Build your first three meta-prompt templates
  4. This month: Establish the end-of-session context update as a habit

That's it. Start small, build the habit, and let the system compound.

Frequently Asked Questions

Q: Does the GSD system work with any AI model, or is it specific to certain tools?

The system is fully tool-agnostic. The principles of meta-prompting, context engineering, and spec-driven development apply whether you're using Claude, GPT-4o, Gemini, or any other capable language model. Some tools (like Cursor) have features that make implementation easier, but the methodology works everywhere.

Q: How long does it take to set up the GSD system for a new project?

For a solo developer, initial setup takes 2-4 hours: roughly 30-45 minutes for the context file, 30-60 minutes for your first spec, and 1-2 hours building your meta-prompt template library. For teams, add time for alignment and shared conventions. Most developers recoup this investment within the first week.

Q: Is the GSD system suitable for beginners, or is it more for experienced developers?

It works for both, but the learning curve is steeper for beginners because you need enough technical knowledge to write meaningful specs and evaluate AI output. That said, the system can actually accelerate learning — writing specs forces you to think clearly about architecture, which builds real engineering skills.

Q: How does context engineering differ from just writing a long system prompt?

A long system prompt is one tool within context engineering, but context engineering is a broader discipline. It involves deciding what information to include, how to structure it, when to update it, how to handle context limits, and how to maintain consistency across sessions and team members. Think of context engineering as the strategy; the system prompt is one tactic.

Q: Can the GSD system be used for non-coding tasks, like content creation or data analysis?

Absolutely. The core principles — meta-prompting, structured context, and spec-driven execution — apply to any complex AI-assisted workflow. The specific templates and tools will differ, but developers have successfully adapted GSD for technical writing, data pipelines, API design documentation, and even product roadmapping.

Ready to Actually Ship?

The Get Shit Done system isn't magic. It's discipline applied to a powerful tool. The developers who are pulling ahead right now aren't the ones with access to better AI — they're the ones who've figured out how to work with AI systematically.

Start with your PROJECT_CONTEXT.md file. Write your next spec before you write a single line of code. Build one meta-prompt template this week.

Small habits. Compounding returns. Ship more. Fix less.

What are you building next? Drop a comment below, or [INTERNAL_LINK: join our developer newsletter] for weekly GSD templates, prompt libraries, and workflow updates delivered straight to your inbox.

Last updated: March 2026 | [INTERNAL_LINK: AI development methodology] | [INTERNAL_LINK: prompt engineering guide]

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