The Self-Taught AI Builder’s Playbook: How I'm Learning Automation, Prompt Engineering, and AI Fast

5 ways to supercharge your AI journey

AI is moving fast. If you’re just getting started with automation, prompt engineering, or trying to launch an AI-powered service, it can feel overwhelming or like you’re already behind before you even begin.

I’ve been there. As a content writer pivoting into AI automation, I’ve had to teach myself how to build tools, ship projects, and translate theoretical knowledge into real-world outcomes — all without a technical background or a team behind me.

This post is for anyone navigating that same learning curve.

If you’re serious about learning AI automation in a way that actually sticks, here are five strategies that helped me go from “just curious” to building tools, workflows, and even early product prototypes for real use cases and clients.

1. Build mini-projects around real pain points

The fastest way to learn is to build. Not read, not bookmark videos, not wait for the “perfect” idea, just pick a real task and automate it.

For me, that task was research. I love reading and learning new things, but there’s way too much content online and never enough time in the day to read it all. So I built a personal research assistant: an automation that scrapes an article, runs it through GPT-4, and summarizes the key insights into a Google Sheet.

It started as a weekend test, now, it’s part of my daily workflow. If I find something interesting, I just plug the URL into the automation and within seconds, I’ve got a summary with the key facts and takeaways. It didn’t even take long to build.

What made it effective wasn’t polish. It was utility.

Start like this:

• Choose a repetitive, time-consuming task in your work or life.

• Use low-lift tools like Make.com, n8n, Zapier, or Voiceflow.

• Focus on solving a real problem, not building something fancy.

Quick mindset shift:
Theory is optional. Output is not. You’ll learn way more from shipping something small that works than sitting through hours of videos.

2. Learn by watching the right builders, not bingeing hype

YouTube can be a goldmine but only if you follow the right creators.

Too many AI videos focus on hype, listicles, or viral tricks. The ones that moved the needle for me were those that walked through actual project builds, showed the wiring behind the interface, and broke down what worked (and what didn’t).

Start here:

• Liam Ottley: Founder of Morningside AI and the AI Automation Agency Hub; shares in-depth breakdowns of how to build and sell chatbot automations

• Nick Saraev: Creator of Maker School; built a $25K/month AI automation agency using Make.com and shows others how to do the same

• Matt Wolfe: Curator behind Future Tools; delivers weekly roundups of emerging AI tools and trends for creators and solopreneurs

• Greg Kamradt (Data Indy): One of the most respected voices in LLM development; covers embeddings, retrieval-augmented generation, agents, and production-grade AI stacks

These creators helped me understand how to connect GPT to real-world problems—from building lead gen bots to summarizing PDFs and onboarding workflows.

Tip: Watch with purpose. Pause, apply, test. You’re not watching Netflix. The goal isn’t to finish the video, it’s to finish a build. Even if it’s rough. If you get to the end of a 2-hour video and haven’t opened a single tool, you’ve wasted time. The knowledge won’t stick until you use it.

3. Use niche communities like search engines, not safety blankets

I’m active in communities like r/automation on Reddit

Communities are powerful. But if you’re not careful, you’ll spend more time scrolling threads than building tools.

What helped me most was treating these platforms like interactive search engines, using them to solve problems as they came up.

Communities that actually helped:

Reddit

• r/PromptEngineering: Focused entirely on prompt design patterns, breakdowns, and examples. A great place to see how others are solving creative or technical problems using language alone.

• r/automation: Covers general automation logic, including Zapier, Make, and Python scripts. Good for finding non-AI workarounds and system-thinking insights.

• r/n8n: The go-to forum for people building end-to-end workflows using n8n. Great for real-world debugging, module-specific help, and sharing unique node combination

Discord

• AI Agency Alliance: Geared toward building and scaling AI services. Great for early-stage client acquisition tactics, prompt library sharing, and battle-tested feedback on offers and pricing.

• Learn AI Together: One of the most active AI communities for learners at every stage. You'll find live help, mini-collabs, and people sharing everything from GPT wrappers to Kaggle wins.

Skool

• AI Automation Society – focused on beginners scaling up

• AI Automation Agency Hub (Liam Ottley): Massive community (100K+) built around AI workflows and client delivery. Especially valuable if you're building bots or working with no-code tools.

• AI Automation Society – A curated Skool for beginners and intermediate builders trying to master the fundamentals of automation and LLM use without getting overwhelmed

• Maker School (Nick Saraev): Focused on going from solo builder to $25K/month service provider. Known for clear breakdowns and community wins shared in real-time.

Tip: When you’re stuck, Google or ChatGPT alone won’t. help. Search your issue directly inside these communities. Chances are, someone’s hit the same wall.

4. Take purpose-built courses — not random AI mega-bundles

There’s no shortage of AI courses. The problem is knowing which ones are worth your time. I learned the hard way that collecting certificates doesn’t mean much if you can’t build something useful with what you’ve learned.

The best courses are project-based, outcome-driven, and aligned with your actual goals—whether that’s launching a service, automating a workflow, or deepening technical fluency.

Below are the ones that stood out most in my own learning. All tested, saved, or recommended by builders I follow.

Start with no-code and AI foundations

For beginners, writers, marketers, or operators learning AI automation from scratch:

• OpenAI Academy: The official learning hub for OpenAI tools and APIs. Great for both devs and business users looking to master GPT models, assistants, and workflows.

• AI For Everyone – Andrew Ng (Coursera): Covers AI fundamentals without code. Teaches how to think about AI from a business and ethical lens.

• Modern AI with No Code (Udemy): Walks you through building functional AI tools using platforms like Lobe and Teachable Machine. No technical experience required.

• Reclaim the Future (LangOps): Ideal for service businesses. Teaches AI strategy, workflows, and client communication with real use cases.

• ChatGPT at Work Series – OpenAI: Shows how to use GPT for writing, planning, coding, and content operations. Great for internal teams and content creators.

• Prompt Engineering for ChatGPT – DeepLearning.AI: A focused course on designing prompts for logic, summarization, classification, and productivity.

• Learn Prompt Engineering – Codecademy: Beginner-friendly and interactive. Helps you understand system/user roles, prompt chaining, and format consistency.

• PromptEngineering.org: Free, self-paced, and practical. Covers prompt styles and formats with examples from multiple industries.

Move into prompt engineering and agent-based tools

Once you’ve got a foundation, specialize in more intelligent workflows:

• LangChain for LLM App Development – DeepLearning.AI: Learn to build apps that let GPT query tools, retrieve data, and take action. Very hands-on and practical.

• HuggingFace Agents Course: Covers multi-step reasoning and API interaction using HuggingFace’s new agents framework.

• Claude A to Z – Anthropic: Covers everything from Claude’s prompting structure to use cases and safety features. Very clear and readable.

• Gemini Prompting Guide – Google: A clean, tactical guide that breaks down how to write better prompts for Google's LLMs like Gemini/PaLM.

• Building Effective Agents – Anthropic: A rare gem. Teaches how to structure agents with internal reasoning steps and external tools.

• 50+ AI Agents You Can Launch (GitHub):A packed GitHub repo with ready-to-test examples for solo builders, including RAG, tools, APIs, and workflows.

• OpenAI Build Hours Collection: Deep dives into GPT tool use, including fine-tuning, Assistants API, and complex chained workflows.

Level up with specialized or technical training

If you’re ready to go deep or apply AI in niche contexts:

• CS50’s AI with Python – Harvard/edX: Structured and rigorous. Introduces core AI techniques like search, adversarial games, and machine learning.

• AI Programming with Python – Udacity: Great for anyone transitioning from zero to Python-capable. Covers NumPy, Pandas, and intro neural nets.

• HuggingFace Courses: Covers everything from LLMs and vision to RL, audio, and 3D. All free, open-source, and highly practical.

• Deep Dive into LLMs: One of the best single-session explainers on how LLMs function under the hood. Great for context and depth.

• Perplexity Labs: Explains how to use Perplexity’s summarization and research capabilities for faster, more accurate info retrieval.

• Sora Tutorials – OpenAI: Short demos for using Sora to storyboard, recut, remix, and blend AI video content for marketing or storytelling.

• OpusClip: Tool-specific tutorial for repurposing long videos into short, captioned clips — all AI-assisted.

• No Code AI & ML – MIT Professional Ed: Teaches how to apply machine learning in business scenarios using structured thinking and drag-and-drop tools.

Advice I wish I had earlier:
Pick one technical course and one business course. Don’t try to “collect” all of them. You’ll learn more by building while you take the course.

5. Create content as a forcing function

If you want to really understand what you’re learning, teach it.

With my own Substack, I’ve noticed that explaining an automation forced me to confront what I didn’t fully grasp. That leads to greater clarity and comprehension.

It also builds trust. My most engaged readers and early clients didn’t come from ads or cold outreach — they came because they saw what I was sharing.

Try this:

• Write a short tutorial on something you built

• Post a breakdown on LinkedIn or Substack

• Record a 2-minute walkthrough on Loom

• Share what didn’t work, not just what did
  

Example: This blog post is one of my forcing functions. It helps me consolidate what I’ve learned, and hopefully, helps you shortcut some of the noise.

The path to AI mastery

You don’t need to become an expert overnight. You don’t need to learn everything. You just need to build something real, share it, and repeat.

That’s how I’ve been learning AI and automation. From building, experimenting, and a mix of wins and failures that have actually moved me forward.

Here’s what I recommend:

• Choose one mini-project to build this week

• Pick one YouTube creator to follow and apply their tutorial

• Join one Discord or Reddit community — and use it when you get stuck

• Start your first content piece — even if it’s one paragraph

If you’re serious about AI automation, the fastest way to learn is by doing.
Not waiting. Not watching. Doing.