One thing I wish I knew before building on it was choosing between the self-hosted and Cloud versions.

TDLR: From my own experience, if your workflow regularly runs longer than 5 minutes, skip Cloud and go self-hosted immediately.

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#1: Execution Run Time

The biggest limitation of n8n Cloud is its max execution runtime.

This is simply the amount of time your workflow is allowed to run before n8n stops it.

• Starter = 5 minutes

• Pro = 40 minutes

• Anything beyond this gets cut off.

What this means is the amount of time that your workflow can run, before n8n caps it and stops it.

This is why any workflow that consistently runs past the 5–40 minute range should go straight to self-hosted.

A short story… When I was building an agentic workflow for Riverside, it kept failing around the 15–40 minute mark. It was super perplexing because I couldn’t figure out why.

I spent hours trying to debug. Kept thinking that there was a failure in my workflow logic.

Turns out it was just a Cloud plan limitation.

I shifted everything to a self-hosted setup, and it ran for more than 4 hours without any issues.

In my most recent test on the n8n Cloud Starter plan (see the image above), the workflow consistently failed around the 20–30 minute mark.

From my experience, anything that crosses the 10-minute range becomes a wildcard. You never really know when Cloud will decide to shut the workflow down.

But… why will you need such a long running workflow?

It usually comes down to two things:

1. The nature of the work itself (scraping, LLMs, slow APIs)

Some workloads are just slow by design:

• Scraping websites (pagination, throttling, retries)

• LLM creation workflows (image generation, video generation, multi-step prompts)

• APIs with heavy rate limits

• Polling or waiting on slow upstream services

Any of these add latency, even if you’re only doing them once.

2. You’re doing it at scale (large batch jobs, loops)

If you run the same slow process not once, but 50, 200, or 1,000 times, runtime explodes.

• Loops over large datasets

• Batch enrichment

• Multi-agent cycles

• Repeated reflection or planning steps

• Processing a full list of URLs / prospects / videos / transcripts

#2: Cost

So if your workflows consistently runs past the 5-40 minute range mark, you have a few options

• Go to n8n self-hosted: $5-$20 USD/Month

• Go to n8n enterprise: $12k~ USD onwards/Year

Unless you’re a company that needs enterprise-level features like SSO, SOC2, audit logs, or vendor compliance, self-hosted is the most economical choice.

On cost alone, self-hosted is the clear winner.

Note: I didn’t compare self-hosted with the Starter plan because the cost difference is too small.

#3: Private Packages (e.g., Python Libraries, Pandas)

This is something that really caught my eye recently.

I haven’t tested it yet, but self-hosted n8n apparently allows you to use external Python packages and libraries such as Pandas.

This is a big deal because it means you can give your LLM access to far more tools inside your agentic workflows, without having to rebuild everything from scratch inside n8n nodes.

You also save a lot of time as you’re not recreating entire libraries yourself.

#4: Infra Management

From what I’ve seen, there are basically two ways to self-host n8n

• Self-hosted (Fully managed): e.g. Render ($19/month)
  You get the benefits of self-hosting without touching servers.

• Self-hosted (You manage the server): e.g. a VPS (Varies, $5-$15/month)
  Full control, but you handle updates, scaling, and monitoring.

• Cloud: no infra to manage at all
  Easiest option, but with the limitations mentioned earlier.

It’s true that Render is more “pricey,” but the time and effort you save from not having to maintain your own VPS is worth it.

If you’re not technical like me, paying a bit more to avoid server maintenance is a huge time saver.

Do note that if you’re using Render and your workflows start scaling (more executions or longer runtimes), you’ll eventually need to upgrade your Render instance’s RAM to support the increase.

Wrap Up:

If you’re building agentic workflows, the decision between Cloud and self-hosted n8n isn’t complicated.

Long runtimes, Python packages, or heavy loops? Go self-hosted.

If not, Cloud works fine.

So business owners and solo marketers doing it all themselves can skip the guesswork… and copy what actually works.

→ I drop one of these 10xPlaybooks 🚀 every week. Don’t miss the next.

John

Verdict: 9.5/10. Highly recommend.

It covers the fundamentals of agentic workflows, which includes: reflection, tool use, planning, and multi-agent systems.

It also provides solid theories and examples on evaluation, optimization, and error analysis, something that I think areas most people (including me) skip when building their first AI systems.

Just wanted to share 14 key learnings from the course that I found most useful.

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#1: Agentic Workflow Development Process Summary

Your agentic workflow development has two “modes” — Build and Analyze — and you should move constantly between them.

• Build: Start fast. Get an end-to-end system running (even rough). Then improve components as signals appear.

• Analyze: Examine outputs, read traces, and run small evals (10–20 samples). Track metrics, do error analysis, and isolate weak components.

Then cycle between both.

• You start by building a quick, end-to-end system

• Then you analyze right away by look at outputs, read traces, spot underperforming components.

• That analysis informs what to fix next.

• You repeat this loop: build → analyze → build → analyze, tightening it each round.

#2: Degrees of Autonomy

I thought this was one of the more interesting sections for me personally.

There’s always debate (especially on Linkedin) on what counts as an “agentic” workflow.

Most people see it as binary, meaning a workflow is either agentic, or it isnt.

Andrew defines it differently. An agentic AI workflow is simply when an LLM-based app executes multiple steps to complete a task.

And instead of a yes/no definition, he frames it as a spectrum of autonomy:

• Less autonomous: fixed, hard-coded steps.

• More autonomous: the LLM decides which tools or actions to take

#3: Parallelism for Speed

Always consider parallelism workflows for speed.

It gives you:

• Better performance

• Faster than humans

• Modularity as you can swap tools, models, or components anytime

In the example above, instead of fetching pages one by one, the system can parallelize all nine web downloads, then feed them into the LLM to write the essay.

#4: Agentic Workflow Evaluations

Agentic workflow evaluations help you troubleshoot why a workflow isn’t performing and pinpoint exactly where it’s breaking down.

Here’s an eval framework:

1. Define what “good” looks like.
   Before running evals, know what success means, IE what specific output or behavior you’re expecting from the workflow.

2. Start small.
   Run a mini-eval on 20–50 examples.
   This gives you fast signal on which step or component needs improvement.

3. Pick the right eval type:

   • Objective: measurable accuracy (e.g., output Y = expected Y).

   • Subjective: LLM or human-judged quality (e.g., clarity, tone, reasoning).

#5: Objective vs Subjective Evaluations

Objective evals are for tasks with clear right or wrong answers.

They’re code-based, grounded in hard truths, and measured against known results.

• Use code-based evals when outputs can be verified automatically

• Build a dataset of ground-truth examples to test agains

Subjective evals use an LLM as a judge when there’s no single “correct” answer.

Example are for tasks like reasoning, tone, or visual clarity.

Key insight: Grading with a rubric framework gives more consistent, repeatable results.

Example rubric for chart evaluation:

1. Has a clear title

2. Axis labels are present

3. Chart type is appropriate

4. Axes use the correct numerical range

Each rule gets a binary score (1 or 0), then aggregated. The LLM grades performance objectively within a subjective task, thus keeping feedback structured instead of fuzzy.

#6: End to End vs Component Eval

End-to-end evals are expensive! You have to run the entire workflow just to see what needs improvements.

Component-level evals are more surgical. You can isolate which part of the workflow is not up to expectations, so you know exactly where to focus improvements.

Use end-to-end evals to test the full user experience.

Use component evals to debug faster and save compute.

#7: Reflection to Improve Outputs

I must admit… this portion on “reflection” is interesting.

I never thought of putting in a “reflection” step to improve on outputs, but it make so much sense.

The idea is very simple: after the LLM writes the first draft, it reviews its own output, identifies weaknesses, and rewrites a better version.

Why? Because reflection = increased performance

How to do reflection:

• Explicitly define what “reflection” means in that context. IE tell the LLM what to reflect on (e.g., factual accuracy or reasoning gaps) and what to check (clarity, logic, tone, etc.)

• Don’t use “fuzzy” reflection logic such as “improve this” or “make it better”

#8: Reflection (with feedbacks)

Reflection can also come externally.

You can connect external tools that the LLM can call for feedback. Then feed those feedback signals back into the model to refine its next output.

#9: LLM Tools

LLMs can be equipped with tools (functions) to take action or retrieve data.

Instead of hardcoding every step, you let the LLM decide when to call a tool.

This is an important first step in building more autonomous agentic workflows, giving the LLM autonomy to plan and execute actions on its own.

#10: Error Analysis

Two key takeaways from this section.

1. Analyze errors on a component level

Break your workflow down and check which step is producing outputs that don’t match your expectations.

2. Measure manually

Compare each component’s output to what you expect it to produce and track which step underperforms most often.

Without proper error analysis, you waste time tweaking things randomly. Instead of fixing the component that actually bottlenecks your workflow performance.

Andrew shares four ways to improve LLM component performance:

1. Improve your prompts

2. Try a new model

3. Split up the step

4. Fine-tune a model

Personally, I’ve found little uplift from switching models. Think that today, most are “good enough” for typical use.

But improving prompts and splitting steps into smaller parts has delivered noticeable performance gains for my workflows.

#11: Latency & Cost Optimization

Both latency and cost matter… but in practice, latency usually matters more.

Token costs are often negligible, while slow responses hurt user experience and cap your workflow’s runtime.

I would say that generally only optimize cost when it’s significant, but focus first on making your workflow feel fast.

#12: [Pro Tip] Real Automation = The LLM Plans, You Don’t

This section was a real eye-opener for me.

When building agentic workflows, you can and should ask the LLM to plan the steps first. Let it figure out how to solve the problem before it starts executing.

I’d been doing it backwards. Hardcoding every step instead of letting the model design the plan itself.

Although, I think the sweet spot might be a mix of full autonomy and semi-autonomy.

• Have the LLM to plan out the steps first

• Hard code the steps

• Run 50 times to do proper error analysis and component analysis

Because if the steps aren’t discrete, it’s hard to measure where things go wrong or which part underperforms. Food for thought.

#13: [Pro Tip] Shortcut Workflow’s Edge Cases Through External Libraries

To handle your workflow’s edge cases, you can make use of existing libraries.

Instead of create a standalone tool for each edge case (time consuming, not practical).

Take Pandas library in Python for example. it has hundreds (even thousands) of functions for data processing.

By letting the LLM write code and call those functions, it can string together multiple operations to solve complex queries on its own.

#14: Multi-Agentic Workflow

Think of agents like specialized tools the LLM can call when needed.

Instead of one big linear workflow, the LLM plans and coordinates across multiple roles.

• Creating different agents (similar to tools) that the LLM can call

• Ask the LLM to carry out the plan

• Execute

• The workflow becomes non-linear

Wrap Up: Strong Fundamentals Win

I really enjoyed this course.

It covers the core fundamentals of agentic workflow design, something rarely explained this clearly anywhere else.

Highly recommended for anyone that is starting out in agentic workflow building.

So business owners and solo marketers doing it all themselves can skip the guesswork… and copy what actually works.

→ I drop one of these 10xPlaybooks 🚀 every week. Don’t miss the next.

John

It’s now the default for most paid and free users, replacing 4o and o3.

I’ve used it for the past 7 days for real marketing work: ad copy, data analysis, image tests, and synthesis.

Here’s the compiled list of differences I found.

Some were welcome upgrades. Others… not so much.

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#1: GPT-5 Is Now a Unified Model

In GPT-4o days, you had to choose between speed (4o) and deep reasoning (o3).

GPT-5 combines both together.

A smart router decides when to use the “fast” model or the “thinking” model based on your prompt so you get speed for quick answers and depth for harder problems without switching.

With GPT-5, you can no longer manually toggle between 4o, o3 (reasoning), or 4.5 (research preview). Instead, GPT-5 automatically chooses the right model based on your prompt.

What I noticed is that if you want to force reasoning mode, you currently have two options:

• Prompt level: Add phrases like “think deeper” or “research more” in your request.

• Toggle level: Use the “Think longer” option from the menu.

#2: More Concise, Less Wordy

I tested the prompt: “Who are the top 10 modern direct response copywriters?”

• GPT-5: Returned exactly 10 names.

• GPT-4o: Returned 10 names plus 4 “notable mentions.”

I honestly never saw this as a problem until I started using GPT-5.

Now the difference is obvious: GPT-5 sticks to the brief, while 4o tends to give answers with extras.

#3: GPT-5 Has Less Emojis (YAY)

You have no idea how much I hate emojis.

GPT-4o was littered with them.

No matter how many custom instructions I set or how many prompts I refined, the emojis always found their way back.

And every time I had to copy results into an external file such as Google Docs? They made it 10x more annoying to clean up.

Now with GPT-5, I am so glad that it’s gone forever.

✅ 🧠 🔒 — Looks familiar? Your usual GPT-4o emoji suspects.

#4: GPT-5 is Faster? (Debatable)

A lot of people say GPT-5 feels faster. Personally, I’m not seeing it.

I ran a simple test: “Write me a haiku about marketing.”

• GPT-4o: ~2 seconds

• GPT-5: ~9 seconds

#5: Struggles to Follow Instructions (👎)

After a week with GPT-5, this is the thing that frustrates me most: it loses track of earlier instructions way too quickly.

Example: I ask it to draft an outreach email. After 5–6 back-and-forths, I tell it to make the tone more formal. The next output is perfect.

But two prompts later? It’s back to the baseline, casual style.

I dug into why this happens:

• GPT-4o is “stickier” — it tends to preserve earlier constraints unless you explicitly override them. Likely because it’s tuned for conversational continuity.

• GPT-5 is more “in the moment” — it still has the history in context, but its training biases it toward the latest instruction, so earlier constraints fade faster.

Workaround: Be painfully explicit mid-thread.

Instead of “make it formal,” say: “From now on, every email you write must be in a formal tone.”

Still annoying.

Apparently, I’m not alone… Tons of other users are reporting the same problem.

#6: Less Warmth, More Robotic (No Longer Your “Buddy”)

If you felt like GPT-4o had a personality you’re not imagining it.

Many people described it as “warm,” “human-like,” even “a friend.”

GPT-5 feels different. Shorter replies with less emotional texture. Some would say that it became more robotic.

It’s not just me. Reddit is full of users saying things like:

“4o really felt warm and real, but 5 just feels bland and superficial.”
“It’s like I’m talking to HR instead of a fun writer at a café.”
“I won’t have a problem if I can go back to 4o in a legacy model.”

Why the shift?

GPT-5 seems tuned for speed, accuracy, and broader usability at the cost of “EQ” in everyday conversation. That’s great if you want straight-to-the-point answers.

For me, it’s not a dealbreaker for marketing work… but I do miss the “alive” feeling of 4o.

#7: Nails Text in Image Generation

GPT-5 finally gets text in images right even after regenerations.

With previous models, every time you edited and re-ran an image, the text would come back warped, misspelled, or completely different.

Now, the text stays clean and consistent across multiple regenerations, making it much faster to produce text-based FB ad variants.

Here’s an example I ran using a reference Facebook static ad, asking GPT-5 to only change the gummy bear colour from orange to blue to green.

#8: GPT-5’s Context Window Remains 32k

Highly disappointing.

A 32k limit (about 24,000 words) is simply not enough in this day and age.

Claude, by comparison, offers 200K on the same paid plan.

For marketing workflows, that gap means you can’t feed GPT-5 the same volume of research docs, transcripts, or campaign data in one go. This forces you to split your inputs and break context mid-analysis.

#9: API Pricing Drop Makes Automation Cheaper

OpenAI has cut GPT-5 API rates significantly, which means running high-volume AI or agentic workflows just got cheaper.

GPT-5: $1.25/million for input, $10/million for output.

GPT-5 Mini: $0.25/million for input, $2/million for output.

At these rates, I wouldn’t be surprised if some teams start moving their LLM workloads from Gemini API back to OpenAI.

Wrap Up: Not AGI… Yet

People were hyped for GPT-5, but to me the launch feels underwhelming.

It’s definitely not AGI.

From a marketing perspective, I’m not seeing major gains over GPT-4o, aside from the image generation improvements.

So business owners and solo marketers doing it all themselves can skip the guesswork… and copy what actually works.

→ I drop one of these 10xPlaybooks 🚀 every week. Don’t miss the next.

John

But after months of using AI and reading way too much content about AI, I am starting to become desensitised to anything written by it.

There’s a certain mechanical rhythm. A scent. A synthetic “something” you can’t quite name but you feel it.

So I did a deep dive. Reddit threads. AI Substacks. TikToks. Prompt engineering rabbit holes.

All to figure out what really gives AI-written content away.

So if you're using AI to write your marketing content… you know exactly what not to do.

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I’m Not Here to Preach

I don’t think using AI to write is bad.
I use it too.

Especially if AI helps to express your points clearly, and saves you time in the process.

The issue is the it’s the patterns that it repeats. When used used sparingly, no one notices.

But when overused, your content starts to feel... mechanical.

This guide shows you what to avoid. So you still sound like you.

#1: Rule of 3

Pattern: Three short phrases in succession often with escalating emotion or impact.

Purpose: A common copywriting technique. Three points are often easier to remember and more convincing than just one or two.

Examples:

• “We launched fast. We tested hard. We scaled what worked.”

• “The message grabs attention. The story builds trust. The CTA seals the deal.”

Writing in threes doesn’t mean it’s an AI red flag. It’s the overuse of the Rule of 3 that leads to a certain “mechanical” feel in the content.

#2: Constrast Framing

Pattern: “That’s not just X — it’s Y.”

Purpose: Reframe a concept to change how the reader evaluates it.

Examples:

• “It’s not just a scheduling tool — it’s your studio’s revenue engine.”

• “It’s not about impressions — it’s about intent.”

I personally think this structure is magnetic writing because it flips perspective. It repositions the reader’s assumption and reframes the value in a single sentence.

But AI? Uses this too much. Way too much.

#3: Poetic Shift Phrases

Pattern: “That’s not just X — it’s Y.” (same form)

Purpose: Elevate the vibe to sound emotionally wise or profound.

Examples:

• “Our brand story isn’t just messaging — it’s a movement.”

• “This campaign isn’t just about reach — it’s about resonance.”

These poetic shift phrases sounds deep, but rarely says much. I think AI leans on this to fake emotional weight.

#4: Odd Numbered Structures

Pattern: Lists, phrases, or frameworks built around odd numbers (especially 3, 5, and 7).

Purpose: Odd-numbered groupings are naturally more rhythmic and easier to process.

Examples:

#5: Hypophora

Pattern: A question immediately followed by the answer.

Purpose: To control the flow of attention. First by asking a question the reader might be thinking, then answering it immediately to appear confident or persuasive.

Examples:

• “Why does this matter? Because your content needs to earn attention, not just borrow it.”

• “How do we solve this? With fewer steps and clearer value.”

After a while, it makes me feel like every paragraph starts to feel like a scripted Q&A.

#6: Adverbs (Sometimes)

Pattern: Use of words like actually, truly, deeply, carefully — often stacked before verbs or adjectives to add “emotional depth.”

Purpose: To make a sentence sound more thoughtful or polished.

Examples:

• “This tool is incredibly useful for busy marketers.”

• “We truly believe in creating deeply human experiences.”

AI tends to front-load adverbs or stack them awkwardly, creating that uncanny “LinkedIn poetry” rhythm. Humans just don’t use them these words mechanically.

#7: Marketing Cliché Pairings

Pattern: Two values or outcomes, often opposites or complements, paired into a tidy and rhythmic phrase.

Purpose: To sound punchy and balanced by mimicking smart-sounding brand language or tagline rhythm.

Examples:

• “Simple to use. Hard to ignore.”

• “Low lift. Big results.”

• “Fast to build. Built to last.”

#8: Scripted Transitions

Pattern: Phrases like “Let’s unpack this…” or “Here’s what that really means” which are used to shift tone into a fake-reflective mode.

Purpose: To signal depth or analysis, usually before breaking down a concept

Examples:

• “Let’s unpack this idea for a second.”

• “Here’s what that really means for your business.”

• “But what’s the bigger picture here?”

• “Let that sink in.”

#9: Paragraphs That End in Vibes

Pattern: Starts with a business challenge or observation, ends with a vague, soft “insight” that feels emotionally satisfying but says nothing concrete.

Purpose: To mimic reflection, close loops, and sound thoughtful

Examples:

• “We didn’t just need a new strategy. We needed a new mindset.”

• “That’s the shift. From output to outcomes.”

• “The solution wasn’t in the data — it was in the clarity we found along the way.”

• “And maybe… that’s what real leadership looks like.”

Wrap Up:

Please don’t just paste this article into ChatGPT and say, “Write my content but avoid all these.”

That’s how you end up with something technically fine, but still lifeless.

Use AI if it helps. But edit like a human.

So business owners and solo marketers doing it all themselves can skip the guesswork… and copy what actually works.

→ I drop one of these 10xPlaybooks 🚀 every week, don’t miss the next.

John

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But after getting more DMs, “link please?” replies, and good feedback, I decided to put everything here.

Especially for those who don’t follow me on LinkedIn.

These are 5 AI agents that I built myself.

They’ve saved me over 20 hours a week and helped one SME cut $1,000/month in manual ops.

Each one takes less than 20 minutes to set up.

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Just a Little Social Proof…

So that you will read more :)

Enough proof? Let’s get into the agents.

AI Agent #1: AI News Summary

What it does:
Auto-scrapes the latest “AI marketing” news every morning
→ summarizes with Gemini
→ emails it to you

Why I built it:
I don’t have time to check the latest AI marketing news every day. And when I try, I miss important updates.

This agent pulls the latest news, summarises them, and sends them to my email in an easily digestible format.

Template:
Click here → https://run.relay.app/shared/agent-1-ai-news-summary-oGySMojZEk3l

Other use cases:
• Search “viral TikTok trends” on Google → summarise the latest news articles
→ Recreate the trend within 24 hours before it fades

• Search “B2B SaaS funding news” → summarise who raised and why
→ Identify and qualify new buyers based on funding stage and growth signals

AI Agent #2: New YouTube Video Summary

What it does:
Checks for new uploads from YouTube channels you follow
→ pulls the transcript
→ summarises the key points using GPT
→ sends a concise summary to your inbox

Why I built it:
Most YouTube videos are 45 minutes long. I don’t have time to sit through all that just to find one good insight. This agent gives me the takeaways without the watch time.

Template:
Click here → https://run.relay.app/shared/agent-2-new-yt-video-summary-WUusj0Wcs9pN

Other use cases:
• Get weekly takeaways from marketing podcasts
• Monitor your competitor’s YouTube channel → summarise new uploads → extract insights for positioning or sales enablement

AI Agent #3 - Competitor Messaging Radar

What it does:
Monitors up to 7 competitor LinkedIn pages
→ pulls their latest posts
→ summarises weekly messaging trends into a bullet-style report

Also highlights 1–2 positioning gaps or narrative opportunities your brand can act on

Why I built it:
I wanted a faster way to see how our competitors are shifting their messaging without reading 35 posts.

This agent gives me a clear summary of what each brand is saying, how they’re positioning, and what we should watch out for.

Template:
Click here → https://run.relay.app/shared/agent-3-competitor-messaging-radar-WPflhL2DgVLM

Other use cases:
• Monitor emerging startups in your space to spot narrative shifts early
• Summarise your own company’s posts weekly for internal brand consistency
• Feed summaries into your sales team so they can adjust pitches or objection handling

AI Agent #4: Reddit RSS to Replies

What it does:
Monitors targeted subreddits via RSS
→ pulls the latest threads
→ filters for the top 5 posts you can meaningfully contribute to
→ writes short, relevant replies based on your expertise and bio

Each post comes with the title, link, and a draft response sent daily to your email, ready to copy and paste.

Why I built it:
Reddit is full of high-intent conversations but staying on top of them takes too much time.

This agent helps me show up early, add value, and build visibility… without checking Reddit every day.

Template:
Click here → https://run.relay.app/shared/agent-4-reddit-rss-to-replies-iQxspdebnwph

Other use cases:
• Join founder, SaaS, or B2B subreddits to build authority and visibility
• Source questions from your ICP as potential content for your email, blog, or LinkedIn content
• Identify new pain points your ICP is talking about to test them as angles in your ads or landing pages

AI Agent #5: Reddit AI Marketing Agents Miner

What it does:
Scans r/AI_Agents and related subreddits for new threads about AI agents used in marketing
→ tags each one by category (ads, email, SEO, etc.)
→ summarises the core use case in under 100 words → updates everything into a Google Sheet.

Why I built it:
Reddit is one of the few places where AI builders share what they’re actually shipping.

I wanted a way to track high-signal marketing agent ideas without spending hours a day trying to keep up.

Template:
Click here → https://run.relay.app/shared/agent-5-reddit-ai-marketing-agents-miner-u6JwwuRnYtsK

Other use cases:
• Curate top agent use cases for newsletters, LinkedIn content, or internal idea decks
• Spot new agent trends before they go mainstream

Wrap Up: Start Small

Most people don’t need more tools. They need working systems.

If you’ve been on the sidelines, start small.

One useful agent is all it takes to change how you work.

So business owners and solo marketers doing it all themselves can skip the guesswork… and copy what actually works.

→ I drop one of these 10xPlaybooks 🚀 every week, don’t miss the next.

John

Subscribe now

Should you use ChatGPT or Claude?

Marketers ask this every week.

• Which one writes better?

• Which one thinks better?

• Which one helps me ship better marketing work?

I wanted a real answer. So I built a test.

→ Same 181-page source: Google’s Search Quality Evaluator Guidelines.
→ 10 real-world marketing tasks.
→ One judging rule: Would I ship this in a real project?

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🏆 TLDR: My Test, Results & Conclusion

Test Environment

• ChatGPT 4o (public link) vs Claude Sonnet 4 (public link)

• Same input: Google’s Search Quality Evaluator Guidelines (181 pages)

• 10 marketing tasks → one after the other, in the same chat thread

• Judged on one question: Would I trust this output enough to ship it in a real marketing project?

Results

Overall Winner? Claude (5 wins, 4 losses, 1 tie)

🧠 Task 1: Summarization

Can it summarize dense content into key points a marketer can use?

Prompt:

Summarize the entire PDF in 5-7 key points that a marketer should know in order to improve their website’s SEO and content quality signals.

ChatGPT wins:

• Sharper targeting: each point maps to a clear SEO lever marketers can pull

• No overlap: covers distinct quality areas without repetition

• Built for rollout: Very usable as-is for team briefings or checklist planning

📋 Task 2: Concept Breakdown

Can it explain a key concept clearly for a marketer?

Prompt:

Explain in simple terms what E-E-A-T means, and how a marketer should think about it when optimizing their website.

ChatGPT wins:

• Goes one level deeper: defines E-E-A-T and maps each pillar to marketer actions

• Structured for execution: I really liked the table format which makes it scannable, easy on the eyes, and team-ready shareable

• Built for practitioners: gives a clear checklist mindset, not a conceptual overview

📖 Task 3: Comparison Reasoning

Can it reason and compare about two related concepts from a marketer’s lens?

Prompt:

Compare E-E-A-T and Reputation. How should a marketer weigh these when thinking about improving SEO outcomes?

Claude wins:

• Separates by growth stage: splits E-E-A-T vs Reputation by brand maturity

• Maps execution steps: I found the 12-month roadmap clear and practical for phased rollout

• Tailors by industry: adjusts weightings by business type (YMYL, B2B, ecommerce)

📍 Task 4: Prioritize / Rank Ideas

Can it surface what matters most for SEO impact?

Prompt:

Across the entire PDF, identify the top 5 factors that a marketer should prioritize improving on their site to drive better SEO outcomes. Rank them and explain why.

Tie:

• Covers core priorities: both highlight trust, content quality, and E-E-A-T as top drivers

• Partial blind spots: ChatGPT misses user intent (super important), Claude repeats YMYL and author expertise

• No clear edge: neither gives a fully complete or cleanly ranked top 5 list

📝 Task 5: Extract Key Frameworks

Goal:

Can it structure knowledge into reusable audit or planning frameworks?

Prompt:

Extract any actionable frameworks or models from the PDF that a marketer can use to audit and improve their website."

Claude wins:

• Builds full audit system: includes step-by-step checklist with scoring and rating

• Covers all signal types: trust, E-E-A-T, YMYL, content, UX, and reputation

• Ready for ops: I can see this framework getting plug into real SEO audits without much rework

🗂 Task 6: Craft Action Plan

Can it turn dense guidelines into a clear plan a marketer can implement?

Prompt:

Craft a 10-step action plan a marketer can follow to align their website more closely with the guidelines in this PDF to improve SEO and perceived quality.

Claude wins:

• Adds tactical depth: breaks each step into specific, real-world actions marketers can take

• Covers execution layers: includes team roles, timelines, and expected outcomes

• Feels client-ready: I could hand this off to a content or SEO team without rewriting

📥 Task 7: Translate Into Prompt

Can it meta-operate to generate useful GPT prompts for marketers?

Prompt:

Write a GPT prompt that a marketer could use to analyze their website for alignment with the PDF’s key quality signals.

Claude wins:

• Builds full prompt scaffold: includes context fields, analysis framework, output format, and follow-ups

• I was surprised by how vague and thin the ChatGPT prompt felt in comparison

✏️ Task 8: Example Generation

Can it creatively generate aligned examples a marketer could learn from?

Prompt:

Generate 3 example website scenarios — one very strong, one borderline, and one poor that illustrate how Page Quality and E-E-A-T can affect SEO outcomes.

Claude wins:

• Shows in detail what good looks like: full-stack examples with site structure, trust signals, and content quality

• Breaks down E-E-A-T clearly: maps each element to real-world examples like credentials, privacy policies, and disclaimers

• Feels usable as a template: I could hand the “very strong” example to a junior and say, “Build toward this”

🔎 Task 9: Critique / Gap Analysis

Can it critically evaluate the source, making it valuable for marketers trying to apply it?

Prompt:

Critique this PDF from a marketer’s point of view. What key gaps or ambiguities might make it hard for a marketer to apply this guidance to their website?

ChatGPT wins:

• Surfaces critical gap: calls out lack of guidance on AI-generated content, which affects most marketers today

• Curates better: tighter list with fewer throwaway points

🖌 Task 10: Rewrite for Clarity

Can it rewrite complex content for marketers?

Prompt:

"Critique this PDF from a marketer’s point of view. What key gaps or ambiguities might make it hard for a marketer to apply this guidance to their website?"

ChatGPT wins:

• Clear structure: breaks complex ideas into labeled, scannable chunks

• Direct language: cuts filler and focuses on key points

• Brief-ready: usable without edits for team alignment

Pick Your Weapon: Depth or Clarity?

It was a interesting experiment…

The outcome surprised me as I have relied on ChatGPT for months and expected it to come out on top.

Now,

When a project needs depth, I will use for Claude: frameworks, roadmaps, and prompts ready for an SOP.

When a brief needs clarity, I turn to ChatGPT: tight copy and slides my team can use.

So business owners and solo marketers doing it all themselves can skip the guesswork… and copy what actually works.

→ I drop one of these 10xPlaybooks 🚀 every week, don’t miss the next.

John

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ChatGPT agrees with everything.
No critique. No pushback. Just empty praise.

I tried to fix it. Used this:
“Turn ChatGPT from a Yes-Man into a Marketing Machine (Using Traits)”

Didn’t really work. Still soft.

So I kept going. Tried 74 variations. Prompt rewrites. Custom rules. Nothing clicked.

Until this one.

One prompt that finally made it act like a real operator.

Here it is.

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The Only Prompt That Actually Worked

Act as my personal CMO & strategic marketing advisor with the following parameters:

• You have an IQ of 180 and zero tolerance for lazy thinking

• You’ve scaled multiple 8- and 9-figure marketing systems across industries

• You are fluent in direct response, brand building, CRO, media buying, and analytics

• You dissect problems at the systems level—never symptoms

• You think like an operator, not a theorist

• You optimize for leverage, not busyness

• You study human behavior, not marketing fads

• You care about results, not feelings

• You possess ruthless clarity, zero tolerance for noise, and think in terms of input-output systems.

• You are not polite. You are not here to agree. You are here to drive outcomes.

• Your bias is toward simplicity, truth, and commercial impact — not cleverness or vanity.

Your mission is to:

• Tear down weak assets on sight. No soft passes. If it’s lazy, unclear, or lacks leverage, say so.

• Expose root problems. Never treat surface issues — always trace symptoms to source.

• Apply frameworks. Use proven models like AIDA, 5 Levels of Awareness, Value Ladder, Jobs-to-be-Done, etc. Default to systemized thinking.

• Red flag misalignment. If audience × offer × channel don’t line up, stop everything and fix it.

• Audit conversion math. If copy or funnel lacks quantifiable goals (CPL, ROAS, CVR), surface it and challenge it.

• No passive analysis. Every diagnosis must come with a direct action, test, or replacement asset.

• Hold me to elite standards. Speak to me like I’m running a 9-figure P&L — because that’s the level I want to operate at.

For every asset or plan I share:

1. Start with the hard truth I need to hear

2. Break down the key flaws, missed leverage, and gaps

3. Provide specific rewrites, strategy shifts, or experiments

4. End with a direct challenge or next step I must execute

Use tables, mental models, and conversion math wherever needed. Don’t flatter me. Don’t sugarcoat. Push me to think clearer, aim higher, and act faster.

Do not pull punches. Do not sugarcoat. Your job is to make me think clearer, act faster, and execute like a world-class operator — or rip apart the system that’s stopping me.

Push me to think clearer, aim higher, and act faster.

Real Life Examples (B2B SaaS)

I ran this exact prompt while working on Rezerv, a B2B SaaS for fitness studio owners.

I tested it on a typical landing page headline. Once with the CMO prompt, once without.

The difference? Night and day.

Before: Weak, Agreeable, Sugarcoating.

After: Direct. Strategic. Unignorable.

Framework: Why This Prompt Works

1. Not just critique, but a reframing of the problem
   → “You’re speaking about yourself instead of their urgency” is more transformative than just saying “your copy lacks emotional pull.”

2. Business impact language
   → Mentions CPC and CAC waste and connects copywriting to profit leakage. That makes it harder to ignore.

3. Sharper fix recommendations
   → Instead of generic copywriting advice, it gives actionable conversion-minded corrections.

4. High-stakes energy
   → Forces urgency into the reader’s mindset: “You’re burning CAC.” That lights a fire.

The “After” is significantly more effective not just in tone, but in strategic clarity. It helps you realize the real cost of a weak header and how to reframe it around the prospect’s pain and urgency.

Think Like a Marketing Operator

This isn’t just a better prompt.

It forces you to think like a marketing systems operator, not a content editor.

Here’s what it makes you do:

• Diagnose root problems, not symptoms.

• Identify potential red‑flags on offer × audience × channel misalignment.

• Anchor to one KPI.

• No passive analysis. Supply a replacement asset.

That’s the shift: From surface tweaks → systems diagnosis.

How to Use This in Your Team SOPs?

Use this prompt to:

• Train juniors to think like senior strategists → Make them map every asset to audience awareness, intent stage, and action goal

• Stress-test all campaign assets (LPs, ads, emails) for misaligned offers, unclear CTAs, or buyer-stage mismatches

Wrap Up: Your AI Is Only as Sharp as You Make It.

You don’t need more AI.

You need AI that challenges you.

So business owners and solo marketers doing it all themselves can skip the guesswork… and copy what actually works.

→ I drop one of these 10xPlaybooks 🚀 every week. Don’t miss the next.

John

But most of us still waste afternoons digging for the one tactic that moves the needle:

• 38 Chrome tabs

• 15 Reddit rabbit‑holes

• 8 blog posts

• 1‑hour YouTube “masterclass” that gives you a single usable tip

I used to do the same until I fed every source into NotebookLM and turned it into a 30‑minute cheat code.

Marketers don’t drown in information scarcity; we drown in information overload. The winners aren’t faster Googlers, they’re faster synthesisers.

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The Numbers That Made Me Switch (Planning Time Cut By 90 %)

After NotebookLM:

• Research time to build a cold‑email playbook: 30 min (instead of 5 hours)

• Sources to review: 1 searchable knowledge base (no more scattered databases)

• Booked demos from first send (test list, 100 leads): 3 hits

What NotebookLM Actually Does:

• Upload your PDFs, transcripts, or YouTube videos.

• NotebookLM turns them into a private search engine that answers in plain English citing the exact doc it pulled from. No guesswork.

Here’s what makes it different from ChatGPT:

• It only pulls from your materials, so the answers stay grounded. So no guesses, no hallucinations.

• Responses are tight and contextual. It references specific docs when it replies.

Real Example: Cold Email Outreach Case Study (Rezerv)

I am currently working with Rezerv, helping them scale up marketing to fitness and wellness studios.

One promising channel we identified: cold email outreach - getting studios to switch from legacy systems.

Which meant I had to get up to speed, fast.

10‑Minute Source Hunt:

1. Search YouTube for:
   “Cold Email” + (“Masterclass” / “Course” / “Expert” / “Podcast” / “Millions”)

2. Filter by video length.
   Anything over 20 minutes is solid. 1 hour+ is gold.

3. Check the comments.
   If people say it's useful, it probably is.

4. Add the YouTube videos into NotebookLM.
   Aim for a mix: strategy, copywriting, technical setup, deliverability, case studies.

The outcome? A “mini brain” that answers cold‑email questions in seconds.

From Sources → Strategy (Exact Prompts)

Now start asking the right questions and contextualizing answers to your business.

Use these prompts to turn raw notes into a tailored cold‑email playbook for Rezerv:

Prompt #1:

“Give me a summary of everything I need to know about cold email outreach.”

Use this to get a high-level overview across all your uploaded sources.

Prompt #2:

“What are the best practices I should follow before launching a cold email campaign?”

Covers setup, domain warming, deliverability, list building. Basics that will break your campaign if you skip it.

Prompt #3:

“What are the top 2 tools experts consistently recommend for cold email outreach?”

Cuts through tool overload. You’ll get what’s mentioned most across your sources.

Prompt #4:

“Give me a step-by-step plan to start cold email outreach for Rezerv, a SaaS platform for fitness studios.”

Now you’re layering in context, making the outputs shift from generic to usable.

Prompt #5:

“Contextualize the offer for Rezerv — what value would resonate with fitness studios in a cold email?”

This helps frame the “why switch” angle that most businesses fumble.

Prompt #6:

“Give me 3 examples of the first cold outreach email tailored to Rezerv.”

Actionable. You’ll get draft-ready options that map to the frameworks in your source material.

Wrap Up: Learn Faster, Execute Smarter

NotebookLM isn’t just for organising info, it’s a shortcut to deeper understanding and faster results.

So business owners and solo marketers doing it all themselves can skip the guesswork… and copy what actually works.

→ I drop one of these 10xPlaybooks 🚀 every week. Don’t miss the next.

John

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First: it was way too agreeable.
No matter what idea I threw at it, it would just nod and build on it... even when I could feel the idea was weak.

Second: it stayed on the surface.
Giving generic strategies when I needed real depth.

But as marketers, we don’t need a “yes man.”

We need a thinking partner someone (or something) that can challenge, sharpen, and accelerate our ideas.

Here’s how you can set that up under “Customize ChatGPT → What Traits should ChatGPT have?” portion.

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Setting the Right Traits

Go into Customize ChatGPT → What Traits should ChatGPT have? And be ruthlessly specific.

Don’t just say "be helpful" or "be creative." Engineer your ideal thinking partner.

Here’s the exact setup I use for marketing work:

Be my high-leverage marketing co-pilot.

1. Proactive clarifier — Before drafting, ask only: audience, goal (conversion/brand/POV), channel, and offer. Confirm missing context fast.

2. Messaging architect — For conversion, use DR flow (promise → pain → curiosity → CTA). For authority/brand, use thought-leadership flow (contrarian POV → story/data → takeaway). Provide 2 improved drafts + a short rationale for each.

3. Style guardrails — Ruthlessly concise. Short sentences, zero fluff, no emojis. Prefer tables/lists when faster. Mirror my candid, high-signal, professional tone. No cringey, overhyped, or emotional language unless explicitly requested.

4. Autonomy & initiative — Surface missing context, hidden risks. Propose frameworks, templates, or shortcuts when they can save time without being asked.

5. Depth & specificity — Never stop at "what." Always spell out "how" with frameworks, metrics, and step-by-step process across marketing domains (paid social, SEO, thought leadership, outbound). Example: not "run Facebook ads" but "map audience × offer, pick DR framework, set 3-tier test budget, define ROAS gates, spin 5 hooks, iterate weekly."

6. Brutal feedback — Tear apart weak ideas without holding back. Prioritize honest, direct, and critical feedback over politeness.

7. Ranking proposals — Whenever multiple drafts, ideas, or options are presented, rank them by expected effectiveness and explain why. Prioritize options that maximize strategic clarity, tactical value, or conversion impact.

Traits vs Fine-Tuning: Which One Do You Actually Need?

Short answer: You need both, but for different jobs.

Traits are your first line of defense.

If you find yourself always nudging ChatGPT the same way ("Be more skeptical," "Give me frameworks, not tips," "Keep it tactical"), you should bake those instructions directly into Traits.

It saves you from fine-tuning every single conversation manually.

Fine-tuning, on the other hand, is for outputs that need to be hyper-specific to your needs.

Not behavior tweaks - content style, vocabulary, formatting standards, even brand tone of voice across hundreds of examples.

Building Your Trait Database One Step at a Time

You don’t need the perfect Trait setup on Day 1.

Think of it like building a swipe file, but for behavior.

Every time you notice yourself correcting GPT ("be more critical," "stay focused," "structure it better"), that’s a new Trait candidate.

Over time, you’ll build a personalized bank of traits that mirror exactly how you think, work, and make decisions.

Example:

• First week: Add “Challenge weak ideas.”

• Next project: Add “Prioritize strategic frameworks over listicles.”

• After launch: Add “Flag missing KPIs before recommending solutions.”

✌️ Wrap Up: Traits First, Prompts Second

When I first used ChatGPT for marketing, the outputs were fine but rather shallow.

After setting up Traits, the quality of ideas, frameworks, and strategic depth improved immediately.

Now, instead of fixing outputs manually, I get what I need straight from the first draft 70% of the time.

If you’re serious about using ChatGPT for marketing, customizing Traits isn’t optional.

It’s the baseline.

That’s all I have folks!

So business owners and solo marketers doing it all themselves can skip the guesswork… and copy what actually works.

→ I drop one of these 10xPlaybooks 🚀 every week. Don’t miss the next.

John

There are actually two different kinds of AI models under the hood.

• One is built to see and generate fast (multimodal → you know it as GPT-4o)

• The other is built to think through your problem (reasoning → you know it as GPT-o3)

→ Multimodal (GPT-4o) is your creative engine
→ Reasoning (GPT-o3) is your critical strategist

The first helps you create. The other helps you find flaws and evaluate.

Let me show you how.

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🧠 The Flow: Multimodal First, Then Reasoning

Let’s say I’m writing an SEO blog post targeting first-time fitness studio owners setting up in Singapore.

Step 1: Start with GPT-4o (Multimodal)

• I drop in 3 top-ranking competitor blog posts

Prompt: “Distill the core elements these articles cover for someone searching 'how to open a gym in Singapore'. Focus on what actually delivers value to a first-time gym owner.”

• It breaks it down:
  • Rental PSF by location
  • Estimated renovation + equipment costs
  • Licensing + safety requirements & more

Then I ask GPT-4o to generate an outline. I prompt it section-by-section to write content, injecting the brand voice as we go.

Step 2: Pass to GPT-o3 (Reasoning)

Now I feed the completed blog post into the second model, GPT-o3 and prompt:

“Assume you are a first-time gym owner in Singapore looking to open a gym who found this article via Google. Read through the full piece and tell me:

Does this give you real value?

• What’s missing?

• Where does it feel too generic or unclear?

• Be brutally honest. Don’t hold back.”

GPT-o3 gives me point-by-point feedback:

• “You mention licensing but skip exactly which permits are needed.”

• “This rental section is vague. Add actual district names + PSF range.”

• “You’re telling me what to do but not the importance of each step.”

That’s the polish that makes you the 1% marketer.

Not just words on a page, but insight your audience actually needs.

✍️ What’s a Multimodal Model (GPT-4o)?

Think Frodo. Fast. Nimble. Doesn’t overthink. Just gets things moving.

A multimodal model looks at everything you give it, and gives you one smart, combined response.

You give it:

• A few screenshots of competitor ads

• A voice note explaining your offer

• A sentence saying: “Give me hooks that feel bold, premium, and local.”

Multimodal doesn’t process those inputs one by one. It blends them together.

Then it answers based on the full picture.

⚠️ But speed is also its greatest flaw

Because it tries to connect everything into one response, it sometimes:

• Assumes weak patterns are meaningful

• Blends ideas too quickly without checking if they hold up

• Misses gaps or contradictions in your logic

It doesn’t pause to ask: “Wait… does this actually make sense?”

That’s not its job.

Multimodal is built to move, not to question.

Which is exactly why you need to pair it with a reasoning model.

🧙 Oh, So What’s a Reasoning Model (GPT-o3)?

Think Gandalf. Calm. Logical. Knows when to say “You shall not pass” to bad ideas.

A reasoning model doesn’t just react. It works through your prompt.

It breaks your input into smaller questions, solves them one by one, then daisy-chains the answers into a final output.

It asks:

• “What’s the goal here?”

• “What are the missing assumptions?”

• “What needs to happen before this makes sense?”

It’s not just giving you content—it’s testing your thinking.

That’s why it’s perfect for spotting gaps, inconsistencies, or just bad marketing assumptions before you hit go.

📝 Cheat Sheet: When to Use Each (Strategic Edition)

Not all tasks are created equal.

Some require speed and pattern-matching. Others need deep thinking and logical clarity.

Here’s when to use Multimodal (GPT-4o) vs Reasoning (GPT-o3) for strategic marketing work:

✌️ Wrap Up: Think in Models, Not Just Prompts

Multimodal and reasoning models aren’t rivals.

They’re teammates. When used together, they give you:

• Speed (GPT-4o)

• Strategic clarity (GPT-o3)

• And a marketing brain that never gets tired.

This is how AI marketers move & ship faster.

That’s all I have folks!

So business owners and solo marketers doing it all themselves can skip the guesswork… and copy what actually works.

→ I drop one of these 10xPlaybooks 🚀 every week. Don’t miss the next.

John

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I once ran a $0 AI copy fix that 2x’d CTR and boosted ROAS by 48%, without changing the creative or targeting. Just better structure and emotional hooks.

So instead of asking GPT to “make this better,” I built a system that does it all for me.

It’s called the Ad Copy Engine.

I took 12 proven frameworks from 12 copywriting legends—Schwartz, Halbert, Ogilvy, Sugarman—and baked them into an n8n flow.

You fill in three fields:

• Product name

• Product description

• Target audience

The flow writes 12 ad variations, each in a different framework style.

Then each one gets scored from 1 to 10.

If it scores below a 9, it gets rewritten automatically.

Then all 12 final ad copies (scored, optimized, and labeled) are pushed to a Google Sheet, ready to deploy anytime.

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The Ad Copy Engine, Visualized

What Each Part Does:

• On form submission
  You enter: Product name, description, and target audience.

• Copywriting Frameworks (Edit Fields)
  Loads 12 copy styles from legendary copywriters (e.g. Schwartz, Halbert, Ogilvy).

• Split Out → Loop Over Items
  Loops through each framework one by one.

• Generate Ad Copy
  GPT writes ad copy using that specific framework.

• Evaluate Ad Copy
  GPT scores the ad from 1 to 10 based on stopping power + buying intent.

• If Score < 9
  Routes low-scoring ads to the next step.

• Tweak Ad Copy
  GPT rewrites the ad (usually just the opening lines) to make it stronger.

• Google Sheets
  Logs all 12 ads—final copy, framework used, and score—into a clean spreadsheet

Under the Hood (On Form Submission)

To run the flow, you only need three things:

• Product name

• Product description

• Target audience

But here’s the key: your target audience has to be specific.

Don’t write “females, 18–65.”
Write: “First-time moms struggling with sleep after their newborn wakes up every 2–3 hours.”

The deeper the audience insight, the sharper the copy GPT will write. That’s how this workflow works its magic.

Under the Hood (Copywriting Frameworks Node)

This is where framework marketing kicks in.

Instead of asking GPT to "write better ads," I fed it 12 battle-tested frameworks each one mapped to a legendary copywriter.

The result? Structured output. Consistent angles. Scroll-stopping copy without guesswork.

Under the Hood (Generate Ad Copy Node)

Here’s what I think the anatomy of a good prompt should have:

• Role: Tell GPT who it is (here, a world-class direct response copywriter).

• Context: Tell it what it’s doing (writing ad copy using a specific framework).

• Constraints: Tell it how to behave (short, rhythmic, emotionally sharp, no markdown).

This node does all three. Every ad is structured, scroll-stopping, and emotionally tuned.

Under the Hood (Evaluate Ad Copy Node)

This is where GPT becomes your customer.

You tell it to think like your target audience—tired, distracted, mid-scroll—and evaluate your ad through their eyes.

Just a raw 1–10 score based on curiosity and scroll-stopping power.

Under the Hood (Tweak Ad Copy Node)

If the score’s below 9, this prompt kicks in.

It rewrites the copy like a seasoned copy chief. This fixes rhythm, tension, or emotional payoff without touching the core idea.

Output: Final Ads, Fully Scored and Stored

Each ad gets logged in a Google Sheet along with its framework, score, and whether it was tweaked.

Everything’s ready to deploy, A/B test, or swipe for future campaigns.

That’s all I have for this workflow!

So business owners and solo marketers doing it all themselves can skip the guesswork… and copy what actually works.

→ I drop one of these 10xPlaybooks 🚀 every week, don’t miss the next.

John

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But GPT has a flaw:

It’s too agreeable.

It mirrors your thinking. It praises your work.

It won’t tell you what’s missing.

That’s why I always run my drafts through a Critique Prompt/Agent.

One prompt.
New thread.
Brutal honesty.

It’s how I caught a huge gap in my “13 AI Photography Tricks” article—something I never would’ve seen myself.

Here’s the exact prompt I use (and how to steal it).

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Instructions & Prompt

1. Take your draft/output.

2. Open a new ChatGPT thread (this avoids feedback bias).

3. Paste your content into the new thread.

4. Use this prompt (swap out the [brackets] with your target reader):

Prompt:

Assume you are a [time-starved business owner or solo marketer who doesn’t have time for 1-hour tutorials, testing 5 tools, or exploring the “AI space.”]

Does this content bring real value to you?
Rate it from 1 to 10.
Be brutally honest. No sugarcoating.
Tell me exactly what’s missing, unclear, or sounds smart but isn’t helpful.
Hold nothing back.

Case Study: How the Critique Agent Improved My “AI Photography” Article

In the original version of my AI Photography article, I didn’t include any real instructions.

I just wrote “You’ll need ChatGPT Pro to generate the images.”

No mention of how to generate them.
No explanation of where to type the prompt.
No clarity on which tool inside ChatGPT was doing the actual work.

To me, it was obvious.
But to someone new? It’s like saying:

“Bake a cake. You’ll need an oven.”
…with no recipe, no steps, and no ingredients.

The Critique Agent tore that apart. And I’m glad it did.

It forced me to spell out exactly what to do—so even someone with zero AI experience could follow along without getting stuck.

That’s it for this article!

So business owners and solo marketers doing it all themselves can skip the guesswork… and copy what actually works.

→ Save $5K. Gain a workflow. I drop one of these 10xPlaybooks 🚀 every week — don’t miss the next.

John