hibranwar

AI Production for People With Standards

Stop producing slop. Free, structured, no paywall.

105Sessions
14Modules
~150-200hTotal with assignments
0Cost

Stop producing slop. Build a production pipeline with voice preservation, quality gates, and the taste to know the difference. This course assumes you already use AI. It teaches you to use it without embarrassing yourself.

Module 0

Why Most AI Content Is Garbage

The slop epidemic, and what "good enough" costs the internet.

  1. 0.1 The Slop Epidemic
  2. 0.2 Why Slop Ranks
  3. 0.3 Google's Quality Rater Guidelines and the Slop Threshold
  4. 0.4 The Incentive Structure That Produces Slop
  5. 0.5 What "Good Enough" Costs the Internet

Print / Download Module 0 as PDF

Module 1

What Makes Slop, Slop

Forensic markers, the uncanny valley, and the taste gap.

  1. 1.1 Why AI Defaults to Mediocre
  2. 1.2 The AI Voice: Hedging and Filler
  3. 1.3 The AI Voice: False Enthusiasm and List Addiction
  4. 1.4 The AI Voice: Emoji Pollution and Performative Formatting
  5. 1.5 15 Forensic Markers of AI-Generated Content (Part 1)
  6. 1.6 15 Forensic Markers of AI-Generated Content (Part 2)
  7. 1.7 The Uncanny Valley of AI Writing
  8. 1.8 How to Read AI Content Critically
  9. 1.9 Why "Just Edit the AI Output" Doesn't Work
  10. 1.10 The Taste Gap

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Module 2

AI as Infrastructure, Not Magic

The mental model shift, the factory metaphor, and the human gates.

  1. 2.1 The Mental Model Shift
  2. 2.2 The Factory Metaphor
  3. 2.3 Where AI Sits in the Pipeline (And Where It Doesn't)
  4. 2.4 The Human Gates
  5. 2.5 The Math: What AI Infrastructure Actually Costs

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Module 3

Web Interface vs API

Consumer product vs production tool.

  1. 3.1 Consumer Product vs Production Tool
  2. 3.2 What You Lose With Web Interfaces
  3. 3.3 What You Gain With APIs
  4. 3.4 The API Landscape: Principles That Apply Everywhere
  5. 3.5 API Basics: Your First Programmatic Request
  6. 3.6 Cost Comparison: Subscription vs Pay-Per-Token
  7. 3.7 When Web Interface Is Actually Fine

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Module 4

The Workspace

VS Code, terminal, Git, and environment setup.

  1. 4.1 VS Code as Workspace
  2. 4.2 Claude Code and AI Assistants
  3. 4.3 Terminal Basics
  4. 4.4 File Management
  5. 4.5 Version Control with Git
  6. 4.6 Templates You Can Copy
  7. 4.7 Environment Setup

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Module 5

Prompt Engineering

Why "write me a blog post" fails, and what works instead.

  1. 5.1 Why "Write Me a Blog Post" Fails
  2. 5.2 System Prompts
  3. 5.3 Few-Shot Examples
  4. 5.4 Chain-of-Thought
  5. 5.5 Structured Output
  6. 5.6 Temperature and Output Control
  7. 5.7 Context Window Management
  8. 5.8 Prompt Iteration
  9. 5.9 The Prompt Library
  10. 5.10 Multi-Turn vs Single-Turn

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Module 6

Voice Capture & Preservation

Why AI sounds like AI, and how to make it sound like you.

  1. 6.1 Why AI Sounds Like AI
  2. 6.2 Voice Extraction
  3. 6.3 Building a Voice Fingerprint
  4. 6.4 Output Artifacts
  5. 6.5 Voice to System Prompt
  6. 6.6 Testing: Can Your Reader Tell?
  7. 6.7 Voice Across Content Types
  8. 6.8 The Prompt Me Things Process
  9. 6.9 Building a Perspective Bank

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Module 7

APIs as Research Tools

Tavily, Google Search grounding, and the research-then-write pipeline.

  1. 7.1 Tavily API
  2. 7.2 Google Search Grounding
  3. 7.3 News and Data APIs
  4. 7.4 The Research-Then-Write Pipeline
  5. 7.5 Using APIs to Protect From Slop
  6. 7.6 Building a Research Workflow
  7. 7.7 When to Trust API Results

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Module 8

The Pipeline

Seven stages from research to publishing, and where AI must not sit.

  1. 8.1 The Production Pipeline Concept
  2. 8.2 Stage 1: Research
  3. 8.3 Stage 2: Outline and Structure
  4. 8.4 Stage 3: Draft Generation
  5. 8.5 Stage 4: Human Review
  6. 8.6 Stage 5: Editing
  7. 8.7 Stage 6: Formatting and Export
  8. 8.8 Stage 7: Publishing
  9. 8.9 Where AI Must Not Sit
  10. 8.10 Quality Gates

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Module 9

Multi-Agent Workflows

Multiple AI calls, specific roles, agent chains, and error handling.

  1. 9.1 Multiple AI Calls Specific Roles
  2. 9.2 Designing Agent Chains
  3. 9.3 Research-Writing-Editing Chain
  4. 9.4 Parallel Processing
  5. 9.5 The Agent-as-Colleague Model
  6. 9.6 Error Handling
  7. 9.7 Practical Examples

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Module 10

Batch Processing & Scale

From 1 to 100 without quality collapse.

  1. 10.1 From 1 to 100 Without Quality Collapse
  2. 10.2 Batch Architecture
  3. 10.3 Concurrency
  4. 10.4 Cost Estimation
  5. 10.5 Multi-Language Production
  6. 10.6 Template Systems
  7. 10.7 The 558-Title Question
  8. 10.8 When to Stop Scaling

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Module 11

Quality Control & The Human Gate

Catching hallucination, fact-checking, and knowing when to reject.

  1. 11.1 Catching Hallucination
  2. 11.2 Fact-Checking Workflows
  3. 11.3 The AI Signature Problem
  4. 11.4 Editing AI vs Human Output
  5. 11.5 Quality Scoring
  6. 11.6 The Review Process
  7. 11.7 When to Reject vs Edit

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Module 12

USP & Positioning

What makes your content worth reading when AI can write too.

  1. 12.1 What's Your Differentiator?
  2. 12.2 Original Experience
  3. 12.3 Practitioner vs Aggregated Knowledge
  4. 12.4 Data Only You Have
  5. 12.5 The Opinion Advantage
  6. 12.6 Building Your Content Moat
  7. 12.7 The Future

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Module 13

Protecting Your Work & Staying Ahead

The evolving landscape, ethics, legal, and building resilient systems.

  1. 13.1 The Evolving AI Landscape
  2. 13.2 Model Updates
  3. 13.3 Ethics of AI Content
  4. 13.4 Legal Considerations
  5. 13.5 Building Resilient Systems
  6. 13.6 The Practitioner's Advantage

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