Writing Content with AI, Actually: AI Engineering Series, #2

How to Write Content with AI, Actually

A practical guide to building systems for AI-driven content creation that doesn’t suck

Rab Davidson

ImbaPress

2025

© 2025

Table of Contents

How to Write Content with AI, Actually

Foundations: Understanding AI as a Content Partner

Beginner’s Guide

Structured Pass System

The Generative Substrate: Building Your Context Layer

Voice Control and Content Quality

Technical Implementation

Content Pipelines and Automation

Practical Content Types

Wisdom Extraction & Use-Case Framing

Advanced Techniques

Prompt Templates

Content Quality Guidelines

AI Tool Comparison

Monetization Strategies

Use Cases

Title Page

Cover

Table of Contents

How to Write Content with AI, Actually

A practical guide to building systems for AI-driven content creation that doesn’t suck.

Introduction

This book is about moving beyond random prompts and building coherent systems for using AI in your content creation workflow. We’ll cover everything from structuring your prompts to managing context, controlling voice, and implementing practical technical pipelines.

This isn’t your typical 50 prompts to copy-paste guide. It’s a craftsman’s handbook for serious content creators who want to leverage AI as a force multiplier while maintaining quality and originality.

Core Principles

Systems Over One-Off Prompts: Learn structured passes (Big Picture → Outline → Section → Phrase) that keep AI focused and outputs tight.

Curation is Creation: Your role isn’t tweaking AI slop—it’s driving the ship through intentional curation and direction.

The Law of Reflected Voice: The voice you prompt with is the voice you get back. Master tone control for consistent, engaging content.

Never Slop: Practical techniques to avoid generic AI fluff and create content with actual value.

Table of Contents

Foundations: Understanding AI as a Content Partner

How AI actually works with content

Setting realistic expectations

Finding your role in human-AI collaboration

Beginner’s Guide

Getting started with AI writing tools

First steps for effective implementation

Building confidence with AI writing

Structured Pass System

Big Picture → Outline → Section → Phrase methodology

Why AI sucks at rewriting (and what to do instead)

Practical examples of the pass system in action

The Generative Substrate: Building Your Context Layer

Creating a living context outside the chat

Maintaining coherence across multiple sessions

Context graphs and knowledge management

Voice Control and Content Quality

The Law of Reflected Voice in practice

Calibrating tone for different content types

Avoiding generic AI writing patterns

Technical Implementation

Markdown-based workflows and organization

Folder structures and version control with Git

Using multiple LLMs for review and enhancement

Content Pipelines and Automation

Setting up automatable content systems

Technical stack recommendations (CLI tools, scripting)

AI-assisted multimedia production

Practical Content Types

Long-form content strategies

Short-form content automation

Visual content workflows

Audio and podcast assistance

Wisdom Extraction & Use-Case Framing

Getting structured outputs (tags, metadata, shot lists)

Making AI think harder through specific framing

Moving beyond raw text to actionable insights

Advanced Techniques

Using AI to assess and improve existing content

Combining multiple AI models for better results

Ethical considerations and attribution

Prompt Templates

Ready-to-use templates for common writing tasks

Customizable frameworks for different content types

Advanced prompt enhancement techniques

Content Quality Guidelines

Standards for excellence in AI-generated content

Quality control processes

Ethical content standards

AI Tool Comparison

Detailed comparison of popular AI writing tools

Decision frameworks for tool selection

Performance comparison by content type

Monetization Strategies

Business models for AI-generated content

Direct and indirect monetization approaches

Building valuable IP with AI assistance

Use Cases

Example transformations

Industry applications

Practical takeaways for implementation

Who This Book Is For

Content creators looking to use AI as a productivity multiplier

Writers who want to maintain their voice while leveraging AI’s strengths

Side-hustlers and passive income builders creating scalable content

Anyone frustrated with generic AI-generated content and seeking better approaches

Technical Requirements

To follow along with the technical portions, familiarity with basic Markdown and command-line tools is helpful. All techniques can be implemented using free or low-cost tools, with options for various skill levels.

Foundations: Understanding AI as a Content Partner

Understanding AI as a Content Partner

This section forms the foundation of our approach to AI-driven content creation. Before diving into specific techniques, it’s essential to understand how AI actually works with content, what to realistically expect, and how to position yourself in this collaborative relationship.

How AI Actually Works with Content

AI language models like GPT-4, Claude, and others work by predicting the next most likely tokens (words or parts of words) based on patterns they’ve learned from vast datasets. They don’t understand content the way humans do, but they can:

Recognize and emulate writing patterns and styles

Draw connections between concepts

Generate coherent text that follows logical structures

Adapt tone and voice based on inputs

However, they don’t possess: - Real-world experience or judgment - Long-term memory beyond their context window - Original insights (only recombinations of learned patterns) - The ability to verify facts independently

Understanding these capabilities and limitations is crucial for leveraging AI effectively.

Setting Realistic Expectations

When working with AI for content creation, set appropriate expectations:

AI as Amplifier, Not Replacement: AI works best when amplifying your expertise, not replacing it.

Quality In = Quality Out: The quality of your inputs directly affects the quality of AI outputs.

Contextual Limitations: AI models have limited context windows and can lose track of complex discussions.

Hallucination Risk: AI can confidently present incorrect information, requiring human verification.

Diminishing Returns: More AI generation doesn’t always mean better content. Know when to stop.

Finding Your Role in Human-AI Collaboration

The most effective content creation happens when you establish a clear role for yourself in the human-AI partnership:

You as Director: Set objectives, standards, and voice.