Five-Step AI Expertise Ladder
Progress from basic prompting to purpose-built AI applications.
- Difficulty
- Advanced
- Time to result
- ~months to results
- Steps
- 5
- Confidence
- 99%
The ladder sequences AI adoption through five increasingly capable levels: prompting, onboarding through context, templates, agents, and applications. Each level produces the foundation for the next. A prompt proves that AI can perform a task; documentation defines what success looks like; a template captures transferable expertise; an agent repeats the stable workflow; and an application coordinates complex logic or multiple data sources. The mechanism prevents premature automation because users must first understand and describe the work. Progress depends on rapid iteration rather than a perfect first attempt, and the final goal is not merely using AI but building repeatable systems that extend human expertise, creativity, and execution capacity.
Origin
Extracted from Marketing Against The Grain, where the hosts condensed two years of AI experimentation into a five-step progression from novice use to advanced applications.
Core principles
- 01Master simple interactions before automating them.
- 02Document quality standards so AI can reproduce good work.
- 03Turn proven expertise into reusable templates.
- 04Automate stable prompts with agents.
- 05Use applications when workflows require greater complexity or multiple data sources.
How to run it
- 1
Prompt the Task
Start with a direct request and test whether the task can be completed through a conversational prompt. Iterate until the desired output is achievable.
Pro tip Ask the model to draft or improve the prompt for you.
Watch out Do not automate a task that you cannot yet produce reliably through prompting.
- 2
Onboard with Context
Document the role, task, relevant knowledge, and examples of successful work. Give the AI only the context it needs to understand the assignment and quality bar.
Pro tip Improve the corresponding human onboarding documentation at the same time.
Watch out Weak or ambiguous documentation will produce inconsistent AI performance.
- 3
Build Templates
Reverse engineer strong examples and encode their mechanisms in reusable instructions. Adapt the resulting template to your own voice, standards, and goals.
Pro tip Tell the model to create a template another AI assistant could execute.
Watch out Do not treat templates as permission to copy another person's finished work.
- 4
Create Agents
Place a stable prompt and its supporting templates into an agent that can repeat a defined process. Connect the agent to the inputs and outputs required by the team.
Pro tip Begin with one narrow process that already works manually.
Watch out An agent will scale flaws in an unproven prompt or template.
- 5
Develop Applications
Build an application when the workflow requires multiple data sources, interacting functions, persistent state, or substantial complexity. Use AI coding tools to make development accessible.
Pro tip Choose a point solution tied to one consequential business problem.
Watch out Do not build a large application when a prompt or narrow agent would solve the problem.
In the wild
The host learned successful short-form content patterns, encoded them as templates, and built an agent that categorized YouTube transcripts and drafted LinkedIn posts. The workflow combined prompting, documented categories, templates, and repeated agent execution.
→ Monthly LinkedIn impressions reportedly increased from hundreds of thousands to multiple millions.
A marketer uses AI coding to combine S&P 500 company data with public marketing metrics, calculate marketing scores, rank companies, and examine how those scores relate to growth over time.
→ An idea that previously appeared inaccessible becomes a buildable data application.
Common mistakes
Skipping Direct Task Validation
Building an agent or application before proving the underlying prompt works adds complexity without establishing that the task is feasible.
Automating Ambiguity
If the team cannot clearly document the work or identify good output, higher levels of automation will reproduce that confusion at scale.
Stopping at One-Off Outputs
Repeatedly prompting the same stable task misses the opportunity to templatize and automate it.
Is it for you?
Best for
Teams and individuals who want to build AI competence through increasingly powerful real-world workflows.
Not ideal for
Users seeking a single tool recommendation without investing in documentation, experimentation, or workflow design.
From the transcript
“We went from prompting, context set in, which is onboarding. We went to templates, we went to agents, we went to apps”
“when I've actually figured out how to do something via a prompt. The logical next step is actually to try to integrate that into an…”
“You kind of graduate to needing an app when you need lots of different data sources. What you're trying to do gets very complex.”
From the episode
From Beginner to AI Expert in 30 Minutes: The 5-Step Framework You Need