How to Use These Docs Effectively

These documentation files are designed with LLM-forward principles in mind, meaning they’re optimized for use with Language Learning Models (LLMs) like Claude, ChatGPT, or Cursor’s AI assistant. This page explains how to get the most out of these docs.

The LLM-Forward Approach

What Does “LLM-Forward” Mean?

LLM-forward documentation is structured to provide complete, contextual information that AI assistants can easily understand and act upon. Instead of requiring you to piece together information from multiple sources, these docs contain comprehensive context within each file. This approach follows the same documentation strategy used by leading AI platforms like Claude, which provides detailed context upfront so the AI can generate better, more accurate responses.

Why This Matters

Traditional documentation:

Requires you to read multiple pages
Makes you connect dots between concepts
Forces you to remember context across sessions
Leads to incomplete implementations

LLM-forward documentation:

Provides complete context in one place
Allows AI to connect concepts automatically
Enables accurate, context-aware assistance
Results in better implementations

How to Use These Docs with LLMs

Method 1: Copy Entire Docs

Best for: Getting comprehensive, context-rich assistance on a specific topic. When to use:

Starting a new implementation
Learning a new concept
Need deep understanding of a topic
Building complex features

How to do it:

Open the relevant documentation file
Copy the entire contents (Cmd/Ctrl + A, then Cmd/Ctrl + C)

Paste into your LLM prompt, prefaced with:

I'm working with CreatorCommerce. Here's the documentation for [topic]:

[paste full doc content]

Now help me [your specific question/task]

Example:

I'm working with CreatorCommerce. Here's the documentation for drops and products:

[paste entire drops-products-data-model.mdx]

I need to build a section that shows a creator's drops. Can you help me write the Liquid code?

Best for: Building features that require understanding multiple concepts. When to use:

Building co-branded experiences (needs data model + storefronts docs)
Implementing drops (needs drops docs + custom fields)
Setting up integrations (needs API docs + data models)

How to do it:

Identify all relevant documentation files
Copy each file’s contents
Combine them in one prompt with clear section headers

Example:

I'm building a co-branded product page. Here's the relevant CreatorCommerce documentation:

=== Data Model Overview ===
[paste data-model-overview.mdx]

=== Drops & Products Data Model ===
[paste drops-products-data-model.mdx]

=== Custom Fields Reference ===
[paste custom-fields-reference.mdx]

Now help me build a product page that:
1. Shows the creator's review/note for the current product
2. Displays the product with custom fields from the drop
3. Shows other products from the same drop

Method 3: Selective Context Extraction

Best for: Quick questions about specific topics. When to use:

Quick reference questions
Clarifying specific concepts
Understanding particular patterns
Troubleshooting specific issues

How to do it:

Find the specific section you need
Copy that section plus relevant context around it
Include any related code examples or patterns

Example:

I'm working with CreatorCommerce and need to match products in drops. Here's the relevant section from the drops documentation:

[paste "Product Matching" section + code examples]

I'm getting a type mismatch error. Can you help me fix it?

Method 4: Progressive Context Building

Best for: Complex implementations that build on each other. When to use:

Building multiple related features
Iterative development
Learning while building
Refactoring existing code

How to do it:

Start with high-level overview docs
Add specific docs as you drill into features
Include your current code for context
Ask for incremental improvements

Example (Session 1):

I'm starting to build with CreatorCommerce. Here's the data model overview:

[paste data-model-overview.mdx]

Help me understand how creator data flows through the system.

Example (Session 2):

Continuing from where we left off. I've got the basic structure. Now I need to access drops. Here's the drops documentation:

[paste drops-products-data-model.mdx]

And here's my current code:
[paste your code]

Help me iterate the drops properly.

Method 5: Complete Context Dumps

Best for: Comprehensive implementations from scratch. When to use:

Building entire features from scratch
Complex implementations requiring full context
Want AI to consider all possibilities
Need optimal, best-practice implementations

How to do it:

Identify all relevant documentation
Copy everything (data models, API references, patterns, examples)
Provide clear, specific requirements
Ask for complete implementation

Example:

I'm building a creator directory grid. Here's all the CreatorCommerce documentation I think is relevant:

=== Glossary ===
[paste glossary.mdx]

=== Creator & Collab Data Model ===
[paste creator-collab-data-model.mdx]

=== Data Model Overview ===
[paste data-model-overview.mdx]

=== Custom Fields Reference ===
[paste custom-fields-reference.mdx]

Build me a complete Shopify section file for a creator directory grid that:
- Shows all public creators
- Displays profile picture, name, and bio
- Links to creator pages
- Filters by tags (optional)
- Is responsive and accessible

Best Practices for LLM Prompts

1. Always Include Relevant Context

Don’t do this:

How do I access creator drops?

Do this:

I'm working with CreatorCommerce. Here's the drops documentation:

[paste drops-products-data-model.mdx]

How do I access creator drops in Liquid? I'm on a regular product page, not a metaobject page.

2. Be Specific About Your Context

Don’t do this:

Help me build a product page.

Do this:

I'm building a product page that shows creator reviews. Here's the relevant documentation:

[paste drops-products-data-model.mdx section on product enhancements]

I need to:
1. Find if the current product is in any of the creator's drops
2. Display the enhancement note if found
3. Show the creator's name and profile picture

Current product context: {{ product }}
Creator context: Available via cart attributes

3. Include Your Current Code

Don’t do this:

Fix this code:
[code with no context]

Do this:

I'm working on accessing creator drops. Here's the CreatorCommerce drops documentation:

[paste relevant sections]

And here's my current implementation:
[paste your code]

I'm getting [specific error]. Can you help me fix it following the patterns in the documentation?

4. Reference Specific Patterns

Don’t do this:

How do I match products?

Do this:

Looking at the "Product Matching" pattern in the drops documentation:

[paste that section]

I'm trying to match products but having trouble with the ID comparison. Can you show me the correct way based on this pattern?

5. Ask for Improvements Based on Best Practices

Don’t do this:

Is this code good?

Do this:

Here's my code for accessing creator data:

[paste your code]

And here's the CreatorCommerce documentation on data access patterns:

[paste relevant sections]

Can you review my code and suggest improvements based on the best practices in the documentation?

Recommended Doc Combinations

Building Co-Branded Storefronts

Combine:

co-branded-storefronts.mdx
data-model-overview.mdx
creator-collab-data-model.mdx
custom-fields-reference.mdx
glossary.mdx

Why: Need understanding of data structures, access patterns, and co-branding concepts.

Implementing Drops

Combine:

drops-products-data-model.mdx
shopify-integration-data-model.mdx
custom-fields-reference.mdx
data-model-overview.mdx

Why: Need to understand drop structure, collection mapping, and custom fields.

API Integration

Combine:

api-overview.mdx
unified-api-reference.mdx
data-model-overview.mdx
glossary.mdx

Why: Need API endpoints, data structures, and terminology.

Building Sections/Snippets

Combine:

data-model-overview.mdx
Relevant data model docs (creator-collab, drops-products, etc.)
custom-fields-reference.mdx
shopify-integration-data-model.mdx

Why: Need data structures, access patterns, and Shopify integration details.

Learning the Platform

Start with:

the-3-ms.mdx - Understand the philosophy
getting-started.mdx - Platform overview
glossary.mdx - Learn terminology
data-model-overview.mdx - Understand architecture
Then dive into specific topics as needed

Copying Docs: Tips & Tricks

Tooling Recommendations

For Desktop:

Use your code editor to open .mdx files
Full file selection is easier in editors
Some editors have “copy file path” features

For Web:

Use the Mintlify docs site if available
Browser extensions for selecting all content
Use “View Source” if available

For AI Tools:

Cursor: Open file in editor, select all, paste into chat
ChatGPT/Claude: Copy from editor, paste in chat
Some tools support file uploads (use .mdx or convert to .md)

Organizing Your Prompts

Use clear section headers:

=== CreatorCommerce Documentation ===

=== Glossary ===
[paste]

=== Data Model Overview ===
[paste]

=== My Question ===
[your specific question]

Use code blocks for code:

Here's the pattern from the docs:
```liquid
{% assign cc_creator = ... %}

And my current code:

[your code]

### Handling Long Docs

**If docs are too long for your LLM:**
1. Break into logical sections
2. Start with overview, add details as needed
3. Use progressive context building
4. Focus on most relevant sections first

**Most LLMs handle these docs fine:**
- Individual docs are sized for typical context windows
- Combine 2-3 docs usually works well
- Modern LLMs handle 50k+ tokens easily

## Why This Strategy Works

### Benefits for You

**Faster Development:**
- Less time hunting for information
- AI can answer with full context
- Fewer iterations needed

**Better Implementations:**
- AI sees complete picture
- Can suggest optimal patterns
- Catches edge cases you might miss

**Learning While Building:**
- AI explains concepts in context
- Can ask follow-up questions
- Documentation is always available

**Consistent Patterns:**
- AI references same patterns
- Encourages best practices
- Reduces code inconsistencies

### Benefits for AI Assistants

**Complete Context:**
- Understands full system
- Can make connections between concepts
- Provides accurate, relevant answers

**Pattern Recognition:**
- Sees consistent patterns across docs
- Can suggest pattern-aligned solutions
- Understands why patterns exist

**Better Code Generation:**
- Generates code that matches platform style
- Includes proper error handling
- Follows best practices

## Real-World Examples

### Example 1: Building a Product Review Section

**Prompt:**

I need to build a product page section that shows creator reviews. Here’s the CreatorCommerce documentation: === Drops & Products Data Model === [paste drops-products-data-model.mdx] === Creator & Collab Data Model === [paste creator-collab-data-model.mdx - sections on access patterns] === Shopify Integration Data Model === [paste shopify-integration-data-model.mdx - sections on metaobject access] Build me a Shopify section file that:

Finds if the current product is in any creator’s drops
Displays the enhancement note if found
Shows creator name and profile picture
Handles missing data gracefully

Section file name: sections/cc-product-review.liquid

**Why this works:**
- AI has complete context on how drops work
- Understands product matching patterns
- Knows access patterns for creator data
- Can generate code following documented patterns

### Example 2: Understanding the Data Model

**Prompt:**

I’m new to CreatorCommerce. Help me understand the data model: === Data Model Overview === [paste data-model-overview.mdx] === Creator & Collab Data Model === [paste creator-collab-data-model.mdx] Explain:

How creators relate to channels/brands
How drops relate to creators and collections
How I access this data in Liquid templates
Common patterns I should know

**Why this works:**
- AI can explain relationships clearly
- Can connect concepts across docs
- Provides examples from the documentation
- Helps you understand the "why" behind patterns

### Example 3: Troubleshooting Code

**Prompt:**

I’m having trouble matching products in drops. Here’s the relevant documentation: === Drops & Products Data Model - Product Matching === [paste “Product Matching” section with ID comparison patterns] And my current code:

{% for json_drop in cc_creator.data.value['cc-creator-drops'] %}
  {% for drop_product in json_drop['cc-creator-drop-products'] %}
    {% assign product_id_str = product.id | append: '' %}
    {% if product_id_str == drop_product['cc-creator-drop-product-shopify-id'] %}
      <!-- Match found -->
    {% endif %}
  {% endfor %}
{% endfor %}

The condition never matches. Can you help me fix it based on the documentation patterns?

**Why this works:**
- AI sees the documented pattern
- Can compare to your code
- Understands the ID type conversion issue
- Can explain why the fix works

## Your Workflow Recommendation

### Step 1: Start with Strategy
Copy `the-3-ms.mdx` to understand the philosophy and orient around what matters.

### Step 2: Learn the Basics
Copy `getting-started.mdx` and `glossary.mdx` for platform overview.

### Step 3: Understand the Data Model
Copy `data-model-overview.mdx` and relevant specific data model docs.

### Step 4: Build Features
Copy relevant docs for each feature you build:
- Building storefronts? → Co-branded docs + data models
- Working with drops? → Drops docs + integration docs
- Using API? → API docs + data models

### Step 5: Reference as Needed
Copy specific sections for quick questions or troubleshooting.

## Tips for Maximum Effectiveness

1. **Don't be shy about copying large amounts** - These docs are designed for this
2. **Include multiple related docs** - AI benefits from seeing connections
3. **Copy your code too** - AI needs context to help effectively
4. **Reference specific sections** - Helps AI focus on relevant parts
5. **Ask follow-up questions** - Build on the context you've provided
6. **Save your prompts** - Reuse successful prompt patterns

## Related Documentation

- [Getting Started](/getting-started) - Platform overview
- [The 3 M's](/the-3-ms) - Strategic orientation
- [Glossary](/glossary) - Key concepts and terminology
- All data model docs - Complete context for implementations

FIRST STEPS

CAPABILITIES

RESOURCES

Documentation Built for AI

How to Use These Docs Effectively

The LLM-Forward Approach

What Does “LLM-Forward” Mean?

Why This Matters

How to Use These Docs with LLMs

Method 1: Copy Entire Docs

Method 3: Selective Context Extraction

Method 4: Progressive Context Building

Method 5: Complete Context Dumps

Best Practices for LLM Prompts

1. Always Include Relevant Context

2. Be Specific About Your Context

3. Include Your Current Code

4. Reference Specific Patterns

5. Ask for Improvements Based on Best Practices

Recommended Doc Combinations

Building Co-Branded Storefronts

Implementing Drops

API Integration

Building Sections/Snippets

Learning the Platform

Copying Docs: Tips & Tricks

Tooling Recommendations

Organizing Your Prompts

FIRST STEPS

CAPABILITIES

RESOURCES

​How to Use These Docs Effectively

​The LLM-Forward Approach

​What Does “LLM-Forward” Mean?

​Why This Matters

​How to Use These Docs with LLMs

​Method 1: Copy Entire Docs

​Method 2: Copy Multiple Related Docs

​Method 3: Selective Context Extraction

​Method 4: Progressive Context Building

​Method 5: Complete Context Dumps

​Best Practices for LLM Prompts

​1. Always Include Relevant Context

​2. Be Specific About Your Context

​3. Include Your Current Code

​4. Reference Specific Patterns

​5. Ask for Improvements Based on Best Practices

​Recommended Doc Combinations

​Building Co-Branded Storefronts

​Implementing Drops

​API Integration

​Building Sections/Snippets

​Learning the Platform

​Copying Docs: Tips & Tricks

​Tooling Recommendations

​Organizing Your Prompts

How to Use These Docs Effectively

The LLM-Forward Approach

What Does “LLM-Forward” Mean?

Why This Matters

How to Use These Docs with LLMs

Method 1: Copy Entire Docs

Method 2: Copy Multiple Related Docs

Method 3: Selective Context Extraction

Method 4: Progressive Context Building

Method 5: Complete Context Dumps

Best Practices for LLM Prompts

1. Always Include Relevant Context

2. Be Specific About Your Context

3. Include Your Current Code

4. Reference Specific Patterns

5. Ask for Improvements Based on Best Practices

Recommended Doc Combinations

Building Co-Branded Storefronts

Implementing Drops

API Integration

Building Sections/Snippets

Learning the Platform

Copying Docs: Tips & Tricks

Tooling Recommendations

Organizing Your Prompts