Building Framework Ecosystems for AI workflows

Hi, Alf here. Happy New Years. I'm excited to share this guide on the insights and key methods I've learned from working with LLMs and AI for 4 years, prompting tens of thousands of iterations.

Through these reps, I've had the opportunity to collect some learnings that have been helpful for me. Hope they will be for you as well. Enjoy.

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Building Framework Ecosystems for AI

This guide is for founders, creators and investors who want to take their AI leverage from basic tool into a systematic thinking partner. Whether you're building products or creating art, you'll learn helpful skills for how to build systems that think the way you think and in ways you’ve never thought before.

During thousands of hours prompting frontier AI models, I discovered that the difference between basic prompts and advanced usage is in the prompting combined with the frameworks that guide the thinking of the models.

Theory: The Foundation

Think of framework ecosystems like teaching a new team member: first you document your processes, then you show them patterns, finally they understand your thinking process. Through systematic organization, frameworks turn surface-level AI interactions into compound value creation.

Three core principles drive this foundation:

1. Structure enhances thinking (like clear, simple to understand content enables learning)

2. Systems beat collections (like playbooks beat random notes)

3. Templates create leverage (like how a checklist speed up work)

Practice: The Implementation

Let me show you how this works with Mind Visuals, a platform for creative asset management. While our primary example focuses on creative workflows, these same principles power systems across industries—from fintech to healthcare protocols.

When building their product documentation system, we started with three essential values:

1. Centralize creative sourcing

2. Improve creative skills

3. Focus creative energy

Each value needed clear context and nuance. Each essential value adds the necessary information for both human and computer understanding. Ultimately creating leverage by adding the necessary information when integrated into a AI workflow

For the value point: "centralized creative source", we developed these docs to integrate into the AI workflow:

• Time-saving asset access (how the library with instant retrieval works, and how to access)

• Quality standardization and hacks (guides for your code app, or "how to import", "how to edit and create" for the Mind Visuals video assets)

• Distribution automation (automated downloads, automated email "loops")

• "Source of truth" (the documents outlining the values and principles of Mind Visuals)

• Road map (document with future ideas and applications, allows the LLM the chance to link the now with the future)

Implementation: In Claude

Here's how to start building your framework ecosystem, regardless of your industry:

PROJECT: Your_System (Automated email loops)
├── Core_Values/
│   ├── Value_One.md (email campaigns and loops.md), your key process
│   ├── Value_Two.md (email specs requirments.md), supporting process
│   └── Value_Three.md (voice, writing guide.md), enhancement process
├── Implementation/
│   └── [Industry-specific frameworks]
└── Integration/
    └── [System connections]

You can use these three steps to activate this system:

1. Create and load core frameworks text files into a Claude Project as "Project Knowledge" (your key processes)

2. Enable value connections by creating a separate document outlining how the frameworks connect together (how processes link)

3. Let patterns be understood by giving context on what areas needs inputs vs where the model can think independently (where automation begins and stops)

The result? Work that used to take weeks of iterative prompting now emerges structured and complete in minutes or hours. Because of our documents with the necessary context and nuance is available to the model, it can quickly "pull" the knowledge and use it iteratively for every of your queries.

Perhaps while prompting and using LLMs we can consider them a "blank slate" for every new interaction that lacks nuance or does not have access to our frameworks and sources. That's why I'm obsessed about the frameworks and sources. We essentially paint out own colors onto the black slate of the LLM model. Eliminating the endless iterative prompting needed to make a blank slate model understand what we're trying to achieve. Whether you're documenting software, analyzing data, or creating content, framework ecosystems transform how AI works for you.

Throughout this guide, we'll build a complete framework ecosystem. Taking your existing blank slated prompting, and adding your own paint to the model by using Claude Projects specifically. Using real examples to show how theory becomes practice. But first, let's look at how to prepare your information for systematic thinking.

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Source Preparation: From Raw Content to Structured Knowledge

Research shows that formatting alone affects GPT-3.5's performance by up to 40%. Think of it like audio quality—the cleaner the input, the clearer the output. Or for learning, the simpler the explanations the quicker you can come up to speed on what you're learning about. Like Feynman's physics lectures, his "sources" were rigorously formatted inside his mind.

Quality preparation becomes desired result. Hand writing is for humans. PDFs (.pdf) and code for human + computer combinations. Markdown and plain text (.md, .txt) for written prompts and sources for AI.

Theory: The Foundation

Mental source preparation, like learning, creates the raw material for editing text or for making an important decision—everything is organized and made ready before cooking begins. With AI, sources transforms raw content into structured knowledge that enables clear and consistent quality thinking from you or an LLM model.

Three principles guide effective preparation:

1. Clean structure enables clear thinking (like organized ingredients enable efficient cooking)

2. Connections create potential compounding value (like an Italian recipe linking to a related Mediterranean ingredient)

3. Systems need standardization (like how humans learn: we synthetize information that connects though logic, structure and necessity)

Practice: The Implementation

Let's see this transformation in action through Mind Visuals, while noting how these same principles work across industries:

1. Source Conversion

Every document follows a specific workflow, whether it's product documentation, financial reports, or medical protocols:

PDFs → Mathpix → Clean markdown

Example transformations:
• Product specs → Structured guides
• Financial data → Analysis frameworks
• Medical protocols → Treatment systems

The goal of converting our files into Markdown is to enhance the efficacy of the documents. Markdown is a plain text format that is simpler that a PDF for a LLM to read. Markdown allow for better hierarchical structure, and faster LLM reading, by way of consistent format, low noise and the use of standard header format for sectioning the text content.

Headers in markdown is read as "#" for Header 1, and "##" for Header 2, and on and on. Learn to use markdown and prompt Claude to "create a clean markdown file with easy to understand and navigate header sectioning, using this document, preserving it's original content…" when in doubt and learn from the outputs. Additionally you can add to the prompt the following "…Finally, optimize this document to be read by an LLM like Claude."

2. Structure Creation

Each document gains clear hierarchy and connection points:

# Asset Management [AM_001]
[PURPOSE: Streamline creative workflow]
[CONNECTS_TO: CREATIVE_001, FLOW_002]

## Core Functionality
- Feature implementation
→ Direct example
→ Connection point

Similar structure works for:
• Financial reports
• Medical procedures
• Technical documentation

By adding clear "code names", you can easily create unique identifiers inside of the markdown documents you are using for this project. Adding the layer of "connects to" enables you as the prompter to directly suggest and guide the LLM on which framework to use next (by referencing the unique identifier), or which framework adds a specific set of context or nuance. We'll dive deeper into how to directly guide the LLM by way of using "system prompts" later.

3. System Integration

Documents become part of larger frameworks, just like:

• Medical protocols linking to treatment systems

• Financial reports connecting to analysis frameworks

• Technical docs integrating with development workflows

You can now clearly see the power of the frameworks painting our desired colors onto our Claude Project. Your imagination is the limitation for the creative use cases for this system.

By the way, you can download this entire guide as a markdown file, and import it into a Claude Project with the attached prompt. This way you can ask Claude questions directly about this system I'm sharing in this post. This is also a perfect opportunity to test and learn about how this works.

Implementation: In Claude

Here's how to implement this in your Projects, regardless of industry:

PROJECT: Knowledge_Base
├── raw/                   # Your source materials
│   └── original_exports/
├── structured/            # Organized knowledge
│   ├── core_processes.md
│   ├── implementation_guides.md
│   └── connection_maps.md
└── integrated/           # System-ready content
    └── framework_ready/

Three steps activate this preparation:

1. Convert sources using clean markdown (like digitizing paper records)

2. Add structure and connection points (like creating a linked database)

3. Deep system maps (like guiding the learning flow of a new apprentice)

To instantly convert old files (PDFs) and images of text into markdown, use Mathpix and create a paid user for unlimited use. This tool is worth every penny.

Every one of our documents are going to be text files. English is the language we're using to explain the blank slated model what we want to do. Optimal format for Claude Projects is markdown (.md). Here's how to create markdown files efficiently:

Option 1: Google Docs Method

1. Create your document in Google Docs

2. Write your content and make sure to create headers by using "styles" and not by increasing text size

3. Click 'Download as markdown' when finished

Option 2: TextEdit Method (Mac) - Recommended for Faster Iterations

1. Open TextEdit → New document

2. Go to Format → Make Plain Text

3. Write your content using markdown syntax (#,##,###, for section structure and headers)

4. Save your file with the .md extension (example: 'framework.md')

The TextEdit method eliminates the need to download files repeatedly, making your iteration process faster and more efficient. Adding the .md extension when naming your file, tells your system to treat the file as markdown, enabling proper formatting when used in Claude Projects.

The result? Content that used to require constant reformatting now enters your system ready for framework integration. Whether you're building product documentation like Mind Visuals, creating financial analysis systems, or developing medical protocols, clean structure enables clear and consistent thinking.

Next, we'll explore how to turn these structured sources into powerful frameworks that actively think.

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Framework Architecture: Building Your System

With clean sources prepared, we can build frameworks that transform how AI thinks. Think of it like building with LEGOs—individual pieces are useful, but a orchestrated architecture creates something greater.

Theory: The Foundation

Framework architecture is like designing a city's infrastructure. Each building (framework) must work independently while connecting to the larger system. Whether you're organizing product documentation, financial systems, or medical protocols, the principles remain consistent.

Three principles guide framework design:

1. Stand-alone value (like each hospital department functioning independently)

2. Connection points (like highways connecting city districts)

3. Pattern enablement (like a server system with end point destinations)

Practice: The Implementation

Let's see how Mind Visuals evolved their creative documentation from isolated pieces to a connected system, with parallels across industries:

1. Basic Documentation (Before)

# Creative Asset Management
- Upload process
- Collection management
- License tracking

Similar to:
• Basic financial procedures
• Initial medical protocols
• Standard operating procedures

2. Framework Evolution (After)

# Asset Framework [ASSET_001]
[PURPOSE: Streamline creative workflow]
[CONNECTS_TO: CREATIVE_001, LICENSE_002]

## Core Process
- Upload and organization
  → Direct integration with tools
  → Automated categorization

## Value Chain
- Time savings through automation
- Quality consistency through systems
- Focus through clear process

Adaptable for:
• Financial reporting frameworks
• Medical treatment protocols
• Technical system documentation

3. System Integration

The framework becomes like a living organism:

• Links to related processes (like nervous system connections)

• Create automated workflows built on top of your frameworks (that explain how to do tasks)

• Creates compound value (the better you become at documenting thinking, the more you can augment and increase leverage with a Claude Project)

Implementation: In Claude

Here's how to build this in your Projects, applicable across industries:

PROJECT: Framework_System
├── Core_Frameworks/          # Your foundation
│   ├── primary_process.md    # Main workflows
│   ├── support_systems.md    # Helper processes
│   └── integration_paths.md  # Connection points
├── Connections/              # System links
│   └── system_map.md
└── Integration/              # Automation
    └── workflow.md

Similar structure works for:
• Product dev
• Investing analysis
• Creative iterations

Three steps requirements for frameworks:

1. Create stand-alone value (like your biceps muscle allowing wrist rotation)

2. Build connection points (like your neurons firing signals to the next muscle in the wrist, following through on the motion)

3. Make the command (like your mind sending the nerve signal down though the arm that initiates the downwards firing)

Creating documentation, sources, and prompts for Claude Projects with rigor is analogous to how we humans can grab a bottle of water without thinking about each nerve and muscle firing manually. We've created autonomous 'programs' inside our minds on how to grab, hold, and twist objects.

What's fascinating is the blank slate of LLMs. They don't necessarily know how to activate the specific 'nerves' (thinking) in the same way you would do automatically. As a human, you've collected not only mental models but principles and optimizations that are your unique identifiers—it's what works for you. This is why we must create documentation of our thinking and develop simplified, structured artifacts that a computer can read. This is how we can paint our own desired skills and patterns onto a model.

The result? A living organism that you can add your own learning, mental models and improve as you like. Whether you're building software documentation, or a founder of a sports club, the same principles apply.

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Next, we'll explore how to create the heart of the AI ecosystems through system prompts, guiding the AI’s thoughts.

Coming to the open access archive, on: FND Labs

For custom access, go to FNDLabs.com

Thanks for taking the time to read and give yourself the edge of AI knowledge few possess.