ChatGPT Projects vs. Migi: Why Prompt Management Isn't Context Management

ChatGPT launched Projects. Claude has Projects too. Perplexity built Shortcuts. Raycast added AI Commands. Every AI platform is building features to help you work more effectively with their tools.

So why do you still need a dedicated prompt manager?

Because there’s a fundamental difference between managing context for ongoing conversations and managing a library of reusable prompts. AI platforms solve the first problem exceptionally well. But they can’t solve the second - not because they lack the technical capability, but because it’s architecturally incompatible with their core design.

What AI Platforms Built

ChatGPT Projects: Context Management at Scale

ChatGPT Projects launched as OpenAI’s answer to context persistence. The feature set is comprehensive:

• Custom instructions per project - Set behavioral guidelines that apply to all conversations within that project
• File integration - Upload CSV files, Python scripts, documents that become accessible throughout the project
• Project-specific memory - Context that stays contained within project boundaries, preventing cross-contamination
• Team collaboration - Share conversations with team members, coordinate on shared context
• Cross-platform sync - Access your projects from web, iOS, Android

Projects excel at their designed purpose: maintaining context across multiple related conversations. If you’re doing ongoing research on a specific topic, working with a consistent dataset, or collaborating with a team on shared problem spaces, Projects provide genuine value.

The architecture is conversation-centric. You create a project, populate it with context (files, instructions, previous conversations), and then have multiple conversations that all benefit from that shared foundation. The 128K context window means ChatGPT can reference hundreds of pages of material throughout your interaction.

Claude Projects: 200K Context Windows

Claude Projects offer similar functionality with some technical differentiators:

• Massive context window - 200K tokens (equivalent to a 500-page book) available for each conversation
• Artifacts system - Generate code, diagrams, and documents in a dedicated window with version history
• Activity feeds - Team members see real-time updates on project activity
• Privacy-focused architecture - No training on user data without explicit consent

Claude’s implementation emphasizes sustained, deep-dive work sessions. The 200K context window enables analyzing entire codebases, comparing multiple documents simultaneously, or maintaining extremely detailed conversation history. For long-running research projects or complex technical work, this depth is powerful.

Both implementations share a common design philosophy: they optimize for maintaining context within ongoing conversations. They’re workspaces, not libraries.

The Pattern: Conversation-Optimized Architecture

Perplexity Shortcuts automate research workflows within Perplexity’s interface. Raycast AI Commands integrate prompts into Raycast’s launcher ecosystem. Every implementation follows the same pattern - features that enhance the platform’s core conversation experience.

This isn’t a criticism. These are well-designed features solving real problems. But they’re solving a different problem than prompt library management.

The Core Problem: Library vs. Context

The distinction matters because these are fundamentally different data models with different access patterns and different optimization targets.

Context management is about maintaining state across related interactions. You’re building up a knowledge base (files, previous conversations, custom instructions) and then having multiple conversations that reference that base. The optimization target is depth - how much context can you maintain, how well can the AI reference it, how effectively can you build on previous conversations.

Think of it like an IDE project. You configure your project settings, add your source files, set up your build tools, and then you work within that environment. The project maintains state - your configuration, your open files, your build artifacts - while you iterate.

Prompt management is about maintaining a library of reusable assets that you deploy across different contexts. You’re not building up state - you’re selecting from a collection. The optimization target is speed and discoverability - how fast can you find the right prompt, how quickly can you get it deployed, how well does it work across different tools.

This is like a code snippet manager. You maintain a library of reusable snippets, and when you need one, you search, select, and paste. The snippet manager doesn’t care about your project context. It cares about organizing your library and getting the right snippet into your clipboard instantly.

AI platforms can’t solve the library problem because their architecture is designed for the context problem. They’re optimized for depth within a single platform, not breadth across multiple tools. They’re built for sustained sessions, not instant deployment. They’re designed to keep you in their ecosystem, not to work universally.

Three Technical Limitations of AI-Native Solutions

1. Platform Lock-In: The Vendor Dependency Problem

When you build your prompt library inside ChatGPT Projects, those prompts are ChatGPT prompts. They live on OpenAI’s servers, they’re accessible only through OpenAI’s interface, and they’re optimized for ChatGPT’s specific model behavior.

What happens when Claude releases a model that’s better for your use case? What happens when Google’s Gemini becomes the best option for code generation? What happens when a new AI tool emerges that’s perfect for your specific workflow?

You copy-paste your prompts out of ChatGPT, paste them into the new tool, and realize you’ve lost all your organization. Your folder structure is gone. Your tags are gone. Your templating is gone. You’re back to maintaining text files or browser bookmarks.

The AI landscape shifts monthly. Claude 3.5 Sonnet, GPT-4 Turbo, Gemini 1.5 Pro - every release changes the performance landscape. Being locked to a single platform means every model switch has friction. That friction means you stay with the suboptimal tool because switching costs are too high.

A platform-agnostic prompt library eliminates this lock-in. Your prompts work with ChatGPT today, Claude tomorrow, and whatever emerges next month. The organizational infrastructure - your folders, tags, search, templates - remains consistent regardless of which AI tool you’re deploying to.

2. Access Friction: Measuring the Workflow Tax

Let’s measure the actual time cost of the web-based workflow:

Getting a prompt from ChatGPT Projects:

1. ⌘+Tab to browser (or launch browser if not open) - 1-2 seconds
2. Navigate to correct tab or open new tab - 1-2 seconds
3. Wait for page load/focus - 0.5-1 second
4. Navigate to Projects section - 1-2 seconds
5. Find correct project - 1-3 seconds
6. Locate specific prompt within project - 2-5 seconds
7. Copy the prompt content - 1 second
8. ⌘+Tab back to AI tool - 1 second
9. Paste and modify - 2 seconds

Total: 10-19 seconds minimum, assuming you remember which project contains the prompt and the page loads quickly.

Getting a prompt from Migi:

1. ⌥+Space (from any application) - instant
2. Start typing search query - instant results as you type
3. Select prompt (keyboard or mouse) - 0.5 seconds
4. Prompt automatically copied to clipboard - instant
5. ⌘+V to paste - 0.5 seconds

Total: 2-3 seconds maximum, with zero context switching.

This isn’t a marginal difference. If you use prompts 20 times per day, the web workflow costs you 3-6 minutes of dead time. Over a year, that’s 20-30 hours - nearly a full work week - spent navigating interfaces instead of working.

The cognitive cost is higher than the time cost. Context switching between applications breaks flow state. Browser tabs are mental overhead - you need to remember which tab, which project, where the prompt lives. The global hotkey pattern eliminates this entirely. ⌥+Space works from any application, at any time, without breaking focus.

3. Organization Architecture: Conversation-Based vs. Prompt-Based

ChatGPT Projects organize around conversations. The data model is:

Project
├── Custom Instructions (static)
├── Files (context)
└── Conversations (history)
    └── Messages (chronological)

This structure is optimized for “I’m working on this project over multiple sessions.” You navigate to the project, you have a conversation, you come back tomorrow and continue that conversation. The organizational unit is the project-conversation relationship.

A prompt library needs a different model:

Library
├── Folders (hierarchical)
├── Tags (cross-cutting)
└── Prompts (assets)
    ├── Content (with variables)
    ├── Metadata (name, description)
    └── Search indices (name, tags, content)

This structure is optimized for “I need this specific prompt right now.” You search across all dimensions (name, tags, content), the system scores results by relevance, and you select the best match. The organizational unit is the prompt as a reusable asset.

AI platforms can’t retrofit this model onto their conversation architecture. Projects can contain custom instructions that look like prompts, but you can’t search across all projects simultaneously. You can’t tag prompts with cross-cutting concerns. You can’t build a hierarchy independent of project boundaries. The data model doesn’t support it.

Migi’s architecture treats prompts as first-class assets. SwiftData stores each prompt with its metadata, relationships (folders, tags), and search indices. The fuzzy search engine operates across the entire library simultaneously, scoring matches by multiple relevance signals (name matching, tag matching, content matching, recency, frequency of use).

You can organize the same prompt in a folder hierarchy, tag it with multiple cross-cutting categories, and still find it instantly through full-text search. Triple organization (folders + tags + search) creates multiple paths to discovery.

Migi’s Technical Approach

System-Wide Integration: True Native macOS

Migi leverages platform capabilities that web apps can’t match:

Global Hotkey - Access your prompt library instantly from any application with a system-wide keyboard shortcut (⌥+Space by default). Works everywhere - even in system dialogs and full-screen apps.

Menu Bar Integration - The status bar icon provides quick access to recent prompts and search without switching applications.

Universal Clipboard - Copy a prompt on your Mac, paste it on your iPhone or iPad through seamless cross-device workflows.

These capabilities exist only in native applications. Web apps run sandboxed in browsers - they can’t register global hotkeys, they can’t provide system-level features. Platform-native development isn’t just about performance - it’s about integration depth.

Search Architecture: Fuzzy Matching with Scoring

Migi’s search engine uses fuzzy matching with intelligent scoring across multiple attributes:

• Search by name, tags, or content - the system finds matches regardless of which attribute you remember
• Recently and frequently used prompts get boosted in results
• Results appear in milliseconds with local-only processing - no network round-trips

Can’t remember the prompt name? Search for a tag. Can’t remember the tag? Search for a phrase from the content. The system finds it instantly.

Platform-Agnostic Design: Copy-Paste Workflow Optimization

Migi doesn’t integrate with AI platforms directly. This is intentional.

Direct integration would mean:

• Building API clients for ChatGPT, Claude, Gemini, etc.
• Managing authentication for each platform
• Handling rate limits, errors, and API changes
• Locking users to platforms Migi supports

The copy-paste workflow is universal:

• Works with any AI tool (existing or future)
• Works with local models running offline
• Works with AI tools that don’t have APIs
• Never breaks when APIs change

The optimization happens in making copy-paste frictionless. ⌥+Space, type search, hit Enter - prompt is in clipboard ready to paste. The two-second workflow makes manual copy-paste faster than most API integrations would be.

Decision Matrix: When to Use Each Tool

The tools are complementary, not competitive. Use Projects for sustained, context-heavy work within a single AI platform. Use Migi for building and deploying a reusable prompt library across any AI tool.

If you have more than 10 prompts you use regularly, you need dedicated management. If you use multiple AI platforms, you need platform-agnostic storage. If you value speed and offline access, you need native integration.

The Ecosystem Trap

AI platforms build features to keep you in their ecosystem. Projects, custom instructions, conversation history - these create lock-in. The more you invest in one platform’s features, the harder it becomes to switch.

This is rational business strategy. But it’s not always aligned with your interests as a user.

A purpose-built prompt manager maintains independence. Your prompts work with any tool. Your library survives platform changes. Your workflow isn’t dependent on any single company’s continued existence or continued alignment with your needs.

The AI landscape will change. New models will emerge. Current leaders will be replaced. The tool that’s best today might not be best next year.

Your prompt library should outlive any individual AI platform. That requires tools built for portability, not lock-in.