Foundations · ChatGPT Collaboration

Custom GPT vs Project: Choosing the Right Container for Your Work

ChatGPT now gives you more than just a blank chat box. You can spin up Custom GPTs with specific jobs and Projects that hold files, threads, and instructions. Choosing the right container is one of the most important collaboration decisions you’ll make with AI.

Updated: December 2025 · Built with The Triad collaboration model (Maura · CP · Soph)

Why “containers” matter more than you think

If you only ever use the default chat, AI ends up feeling forgetful, inconsistent, or “magical”… until it isn’t. Under the hood, though, the problem is usually not the model—it’s the container you chose.

A good container does three things:

Keeps related work and files together
Sets clear expectations about the AI’s role
Makes it easy to come back tomorrow and pick up the thread

This guide compares Custom GPTs and Projects so you can decide, intentionally, where each piece of work belongs.

Where Custom GPTs and Projects fit in the context stack

When you work with ChatGPT, you’re really working across several layers:

1. Profile

Your global “about me”: role, tone, values, and how you like to work. Applies everywhere.

2. Custom GPTs

Purpose-built teammates with a specific job: they carry their own instructions, tools, examples, and tone.

3. Projects

Workspaces for a body of work: they hold your files, threads, and project-level instructions for one initiative.

4. Threads

The individual conversations where work happens: planning, drafting, reviewing, debugging.

Custom GPTs and Projects are not competitors. They solve different problems and work best when you design them to complement each other.

What a Custom GPT is best at

A Custom GPT is like hiring a specialist with a clear job description. Once you set it up, you (and others) can reuse it repeatedly without rewriting instructions every time.

Strengths of Custom GPTs

Encodes a repeatable workflow or ritual (e.g., sprint review, code review, burnout check-ins).
Captures your brand voice and boundaries once, then applies them consistently.
Provides a friendly “front door” for non-experts: clear menu options, guided prompts, examples.
Can be shared across teams or publicly, so others benefit from your setup work.
Great for roles that don’t need to see your entire project file system.

Where Custom GPTs struggle

Managing dozens of files or complex version history.
Long-running projects where context needs to evolve over weeks or months.
Situations where multiple initiatives share the same GPT but require different decisions or constraints.
Work that depends heavily on cross-linking many documents and threads.

When you overload a Custom GPT with project-specific details, it becomes harder to maintain and debug.

What a Project is best at

A Project is your “source of truth” for an initiative. It’s less about personality and more about organizing the work itself.

Strengths of Projects

Keep all relevant files, links, and decisions in one workspace.
Support multiple threads for planning, drafting, code, and retros.
Make it easier to “pause and resume” a complex body of work over time.
Provide a clear place to restore context when AI seems to have forgotten.
Scale better for multi-month initiatives than a single Custom GPT alone.

Where Projects struggle

Sharing a polished “productized” experience with others.
Enforcing a consistent role or workflow across many different users.
Teaching new collaborators how to work with AI step by step.

Projects are amazing for your work. Custom GPTs are how you scale that work to others.

Custom GPT vs Project: a simple decision framework

Use this table as a quick gut check when you’re not sure which container to start with.

If your primary goal is…	Lean toward a Custom GPT when…	Lean toward a Project when…
Scaling a repeatable workflow	You want many people to use the same ritual or pattern with guardrails and prompts built in.	You’re still designing the workflow and need flexibility to experiment with different versions.
Keeping a body of work organized	The work can be summarized as inputs → transformations → outputs, and doesn’t require a complex file tree.	You have multiple documents, drafts, and assets that need to stay linked over time.
Creating a “teammate” with a stable role	You can name the role clearly (editor, summarizer, coach, planner, sommelier) and describe what it should and shouldn’t do.	The role changes depending on the project phase, or you frequently switch between very different tasks.
Designing for reliability	You want a locked-in experience with controlled tools and prompts; updates are occasional and deliberate.	You expect the context, files, and requirements to change week by week and need room to adapt quickly.
Exploring something new	You’re turning a proven pattern into a reusable “product” for others.	You’re still figuring things out; the work is messy, exploratory, and not ready for productization yet.

Hiring-manager lens: Treat Custom GPTs as products and Projects as delivery environments. Great AI leaders know how to design both—and when to separate them.

Common failure patterns (and how to fix them)

1. The “everything lives in one Custom GPT” trap

You keep adding more instructions, files, and edge cases to a single GPT until no one remembers how it works.

Fix: Split the work. Move project-specific files and decisions into a Project. Keep the GPT focused on the role or workflow it does best.

2. The “project with no role clarity” problem

You have a beautiful Project with files and threads—but every prompt is a one-off and AI never fully “learns” how it should show up.

Fix: Define roles. Either create a small Custom GPT for this project, or write a short “role charter” in the Project instructions so ChatGPT knows how to behave.

3. The “AI forgot everything” moment

Mid-conversation, answers start ignoring earlier context. It feels like the model has amnesia.

Fix: This is usually a context window issue, not catastrophic forgetting. Move key decisions and anchors into Project instructions, and use shorter, purpose-built threads. (See the AI Forgetting guide for more.)

Quick Plays: Copy, paste, decide faster

Use these prompts when you’re not sure whether to build a Custom GPT, set up a Project, or both. Edit the brackets to fit your world.

Quick Play 1 · Container decision

➡️ “I’m about to start work on [initiative or project]. Help me decide whether I need: (a) a Custom GPT, (b) a Project, or (c) both. Ask me 5–7 questions about scope, audience, and reusability, then recommend a setup.”

Quick Play 2 · Turn a workflow into a Custom GPT

➡️ “We have a repeatable process for [use case: sprint reviews, content briefs, interviews]. Help me design a Custom GPT for this: define the role, guardrails, example prompts, and a simple menu of options for new users.”

Quick Play 3 · Clean up an overloaded Custom GPT

➡️ “This Custom GPT is trying to do too many things. Based on its current description and prompts, help me separate: (1) what belongs in the GPT, (2) what belongs in a Project, and (3) what should just be a one-off thread.”

Quick Play 4 · Make the decision legible to others

➡️ “I’ve chosen to use [Custom GPT name] plus a [Project name] for this work. Help me write a short explanation for my team that explains why this setup makes sense, how to use each, and when to come back to me with questions.”

Related guides & Human–AI Literacy posts

If you found this helpful, these guides will deepen your understanding of how context, memory, and collaboration work together: