How to Use ChatGPT Projects Feature to Boost your AI Fluency

TLDR: ChatGPT’s “Projects” feature lets you group prompts, files, and tools around a specific piece of work.It’s useful when applied to real client tasks, policy reviews, or campaign builds—but only if you set it up with naming conventions, prompt logging, and human review. This post shows how to use it with structure.

What is ChatGPT’s “Projects” feature?

Projects is a built-in workspace in ChatGPT that lets you group:

• Prompts and conversations
  
• Uploaded files
  
• Tool usage (like file analysis, code interpreter, or browser) into one named area that keeps context across sessions.

Instead of losing track of chats or uploading the same policy PDF five times, you can store and reuse material inside a dedicated space. 

It’s ideal when your team is:

• Drafting multi-step documents
  
• Comparing content versions or iterations
  
• Reusing prompts across similar outputs
  
• Working across AI and human team members
  

But like any new feature, if you don’t set it up intentionally it’ll become just another unsearchable mess.

How should real teams use Projects?

Here’s how we structure it for clients in sectors like marketing, consulting, and government delivery.

1. Use one Project per client deliverable
Example:

• “FY26 Strategic Plan Draft – [Client Name]”
  
• “Grant Application Content – Environment Fund”
  
• “Procurement Process Rewrite – Transport Dept”
  

Don’t group unrelated work together. You want focused, auditable history tied to one outcome.

2. Include structured prompts and version logic
Use a naming convention for your saved chats inside the project:

• “V1 – Initial Draft Prompt”
  
• “V2 – Edited with Legal Tone”
  
• “Summary Prompt – CEO Version”
  

This helps reviewers trace what prompt produced which output, and prevents rework when outputs are challenged or reused.

We teach this structure in the AI Bootcamp using real deliverables from your business.

3. Attach only the essential files
Don’t use Projects as a dumping ground. Upload:

• The most recent source file (e.g. policy, proposal brief, funding guideline)
  
• Any key client instructions or tone references
  
• A human-edited version for comparison
  
• Add notes in the prompt to reference these files clearly.

How to avoid governance and consistency issues

If used carelessly, Projects can introduce new risk.

Here’s what to do everytime to get consistent:

Add a review layer.

Every Project should include one final “review and approval” step by a human.

That means:

• Reviewing final outputs before sharing externally
  
• Checking that prompts align with business tone and brand
  
• Verifying factual accuracy and source traceability
  

If you’re unsure how to formalise this, we map it inside the AI Strategy Roadmap.

Use access intentionally

Don’t share Projects casually across team members. Until OpenAI adds robust permissions, treat these like shared drives with sensitive material.

Set internal rules on:

• Who creates Projects
  
• Who reviews them
  
• How outputs are approved or published
  
• Build a prompt ledger
  

Use a shared doc or Notion page to record: Prompt > Output > File used > Reviewer outcome. This lets you audit AI-supported work and refine prompt patterns over time. 

FAQs

Q: What’s the difference between Projects and a regular ChatGPT chat?
A: A regular chat is disposable. A Project stores multiple chats, files, and tool use under one name keeping memory and context intact over time.

Q: Should every team member create Projects?
A: No. Start with nominated power users. Train them to name, structure, and review Projects properly. Then scale.

Q: Is Projects safe for client or regulated work?
A: Only if you apply internal controls. Limit file uploads to non-sensitive versions, document outputs, and add human review before sharing anything.

Q: Can this replace our document collaboration tools?
A: No. Projects is not a substitute for SharePoint, Confluence, or Google Docs. It’s for AI-assisted draft work—not version-controlled publishing or official records.

Where to Start

• AI Fundamentals Masterclass: Train your team on how to use Projects with real business tasks
  
• AI Bootcamp: Apply the Projects feature to a live workflow and build repeatable output structures
  
• AI Strategy Roadmap: Design policies and review layers for safe, auditable GenAI use
  
• Readiness Assessment: See where tool use is messy or invisible—and fix it before it scales
  

Projects can streamline how your team works with AI but only if it’s designed around real outcomes. Set it up with intent.