AI Portalen

AI Portalen AI Portalen provides free AI courses, practical tutorials, resources, and customized workshops to help individuals and organizations use AI effectively.

Founded by Emma Al. https://www.aiportalen.com

🎁 What if one piece of content could become 8–10?One of the simplest ways I use AI for content marketing is repurposing ...
29/08/2026

🎁 What if one piece of content could become 8–10?

One of the simplest ways I use AI for content marketing is repurposing one substantial piece of work into multiple useful assets.

For example, one article, webinar, or research piece can become:

→ LinkedIn posts

→ a newsletter

→ a video script

→ social media content

→ an email

→ a short educational guide

The important part is that you create the original expertise first. AI helps you adapt and expand it rather than simply generating more content for the sake of generating more content.

This is one of the 5 practical workflows in my AI Content Marketing Playbook.

🎁 And I'm giving the full playbook away for free for a limited time.

It is normally paid content, but I'm making it free for new AI Portalen subscribers until September 20, 2026.

A little about AI Portalen

Many of you here on LinkedIn already read my Substack articles, and I'm incredibly grateful for that. ❤️

But I've noticed that quite a few people follow my work here without actually joining the AI Portalen newsletter. I'd really love to have you there too.

AI Portalen is my newsletter and community on Substack, where I share practical AI guides, tutorials, workflows, prompt techniques, experiments, and resources to help you actually use AI in your work and projects.

So if you've been reading my posts here, this is an invitation to join the community behind them.

👉 Subscribe to AI Portalen:

https://aiportalen.substack.com/

When you subscribe, you are subscribing to my email newsletter. You'll receive AI Portalen posts and updates by email, and you can unsubscribe at any time. There is no commitment.

If you subscribe, you'll receive the playbook in your welcome email. Make sure to download it when you receive it, because the download link will no longer be available after September 20, 2026.

⏰ The playbook is free for new subscribers only until September 20, 2026. After that, it goes back to being paid content.

If you're interested in practical AI rather than AI hype, I'd love to have you join us.

Why does AI sometimes ignore the most important part of your prompt?It may not be because the model doesn’t understand y...
16/08/2026

Why does AI sometimes ignore the most important part of your prompt?

It may not be because the model doesn’t understand your instruction.

It may be because of where you put it.

Research on long-context language models shows a surprising pattern: models often perform better when important information appears near the beginning or the end of a prompt, while information buried in the middle is more likely to be overlooked.

This has some practical consequences for prompt design:

→ Put the task at the beginning and/or end.

→ Keep reference documents in the middle, where they can be looked up.

→ Label documents clearly instead of giving them generic names.

→ Put the most important document first when you have several references.

→ Remove unnecessary context before trying to rearrange your prompt.

→ Separate stable instructions from changing reference material.

And there is an important distinction:

A large context window does not mean the model gives equal attention to everything inside it.

In the article, I break down the lost-in-the-middle effect, context dilution, distractor interference, and show a before-and-after prompt example to demonstrate how the structure changes.

I also explain what this doesn't mean that because the effect is a bias, not a hard rule, and its strength varies by model, task, and context.

If you want the details, research, and the complete practical example:

👉 Read the full article:

https://aiportalen.substack.com/p/why-ai-ignore-your-prompt

Context window ≠ reliable attention.

# Stop Telling AI How to ThinkFor years, we were told to give AI step-by-step instructions. That isn't always the best ...
22/07/2026

# Stop Telling AI How to Think

For years, we were told to give AI step-by-step instructions. That isn't always the best approach anymore. Modern reasoning models from OpenAI and Anthropic can do much of the reasoning on their own, so your prompts can be simpler and more focused.

# # 1. Why reasoning-native AI changes prompt engineering

Older AI models often needed detailed instructions like "think step by step." Today's models already do much of that internally. Your job is becoming less about telling AI how to think and more about telling it what you want.

# # 2. From process-first to outcome-first prompting

Instead of explaining every step, describe the result you're looking for. A clear goal, audience, and expected output often lead to better answers than long, detailed prompts.

# # 3. Context engineering is becoming more important

More words don't always mean better results. Give AI the context it needs, set clear constraints, and explain who the answer is for. Think of your prompt as a clear brief rather than a detailed instruction manual.

# # 4. OpenAI Reasoning best practices

OpenAI now recommends keeping prompts simple, avoiding unnecessary chain-of-thought instructions, and clearly defining the expected outcome. The goal is clarity, not complexity.

# # 5. Anthropic’s guidance points the same way

Anthropic is saying much the same thing. Claude's reasoning models decide how much thinking a task requires instead of following a scripted process. When both leading AI labs give similar advice, it's worth paying attention.

# # 6. The Outcome-First Prompt Template

One simple way to apply this is with the RACE method: Role, Action, Context, and Expectation. Instead of telling AI every step, clearly describe what success looks like.

# # 7. The Modern Prompting Checklist

Before sending your next prompt, ask yourself: Did I clearly describe the outcome? Are my constraints clear? Did I remove unnecessary instructions? Better prompts are becoming shorter, clearer, and more focused.

# # Read the full article

This Facebook post only covers the highlights. The full article includes practical examples, reusable prompt templates, OpenAI and Anthropic guidance, and a complete outcome-first prompting framework you can start using today.

📖 Read the complete article here:
https://aiportalen.substack.com/p/stop-telling-ai-how-to-think-ai-fundamentals

AI Fundamentals1. What Is AI?Ask ten AI researchers to define artificial intelligence and you’ll likely get ten differen...
19/07/2026

AI Fundamentals

1. What Is AI?

Ask ten AI researchers to define artificial intelligence and you’ll likely get ten different answers. That’s not a knock on the field. This section looks at why the definitions diverge, what makes one system “more intelligent” than another, and the philosophical puzzles that still keep “can machines think?” an open question.

Why can’t experts agree on what AI is?

If you’ve ever wondered why experts seem to disagree about what AI actually is, the answer is surprisingly simple: there isn’t one universally accepted definition.

Some definitions focus on behavior: a system counts as intelligent if it acts rationally, choosing actions that get it closer to a goal.

Others focus on the reasoning process itself, asking whether a system thinks the way a human would, regardless of what it does with that thinking.

One helpful way to sort these views is along two axes: is the system judged by its thought process or its behavior, and is it compared to a human or to an ideal standard of rationality? That gives four camps:

thinking humanly, thinking rationally, acting humanly, and acting rationally.

Most modern AI research, including machine learning, falls into the last camp: building systems that make good decisions, whether or not they “think” the way we do.

Take ChatGPT. It can hold a conversation, write code, and summarize a document, but it cannot decide on its own to learn to drive a car or diagnose an illness. That’s because it’s narrow AI: built for a specific range of tasks. General AI (a system with the flexible, cross-domain intelligence of a human) remains hypothetical.

This is Article 1 - AI Fundamentals Series

This isn’t a theoretical deep dive or a university textbook. It’s a practical series designed to help you understand how modern AI works so you can use it more effectively.

We'll start with the basics of what AI is, then explore the concepts that matter in practice: from prompting reasoning models and writing structured prompts with XML to choosing the right AI tool for the job, verifying AI-generated information, and many other topics that will help you become a more capable AI user.

Read the full article here and Subscribe to receive the full series in your inbox:

https://aiportalen.substack.com/p/what-is-ai

18/07/2026

Anyone can copy a prompt.

The real skill is knowing how to build one.

**Role** → Who should the AI be?
**Action** → What should it do?
**Context** → What information can it use?
**Expectation** → What should the result look like?

Understand the framework, and every new prompt becomes easier to write.

Read more: https://www.amazon.com/dp/B0H6PRZPRS

17/07/2026

A prompt is temporary.

A method lasts.

Role.
Action.
Context.
Expectation.

Learn these four building blocks, and you can create prompts instead of searching for them.

https://www.amazon.com/dp/B0H6PRZPRS

16/07/2026

Don't copy prompts.

Learn the method behind them.

**Role** → defines expertise.
**Action** → defines the task.
**Context** → sets the boundaries.
**Expectation** → defines success.

Once you understand the structure, you don't need someone else's prompts—you can build your own.

Learn more: https://www.amazon.com/dp/B0H6PRZPRS

15/07/2026

Stop writing one-off prompts. Build a system.
A reusable RACE template has fixed structure with slots you swap per task — audience, source, format.
Save the ones that work. Organize them into a library.
Going from scattered prompts to a personal prompt system is the leap we guide at AI Portalen.
Learn more: https://www.amazon.com/dp/B0H6PRZPRS

14/07/2026

The biggest productivity gain in prompting isn't writing better prompts.

It's reusing them.

Create one solid RACE template.
Swap the variables.
Test it.
Save it.

Over time, you build a prompt library instead of a collection of random chats.

That's the transition from prompting to prompt engineering.

Read More: https://www.amazon.com/dp/B0H6PRZPRS

Claude for Financial Analysis & Investor ReportingMost startups and small companies don’t have a finance team, often it’...
14/07/2026

Claude for Financial Analysis & Investor Reporting

Most startups and small companies don’t have a finance team, often it’s a founder squinting at a spreadsheet exported from their accounting software, trying to figure out if last month was actually good or bad. Meanwhile, investors and boards expect clear, honest updates on a predictable cadence.

This workflow uses Claude to turn raw financial exports into a structured health check, and then turn that health check into a polished investor update,using the verification techniques introduced earlier in this series, ensuring no figures are invented.

The Freelancer Cost Benchmark

Fractional CFO / Financial Analyst Rate: $150 to $300+ per hour (or $750–$2,000 per board package).

Your Cost with this AI Workflow: $0 (using existing tools) in under 10 minutes.

Scenario A: Analyzing Financial Statements for Trends and Red Flags

Step 1 Export Your Financials: Export your P&L, balance sheet, and cash flow statement (or your accounting software’s report) as PDF or CSV, for the current period and one prior period.

Step 2 Attach the Files: Attach both periods’ statements to Claude, following the Data-First rule we covered before.

Step 3 Run the Financial Health Analysis Prompt:

FINANCIAL HEALTH ANALYSIS PROMPT

1. KEY METRICS: calculate and present Gross Margin, Burn Rate, Runway (in months),

and Month-over-Month Revenue Growth.

2. TREND VARIANCES: flag any line item that changed by more than 15% between the two periods,

with a one-sentence explanation of the likely driver if evident from the data.

3. RED FLAGS — identify any signs of cash flow strain, unusual expense spikes,

or concentration risk (e.g., over-reliance on one customer or vendor) visible in the data.

Present the output as a .docx report with a summary table for Key Metrics,

followed by bulleted Trend Variances and Red Flags sections.

Strict Constraint: base all calculations and observations strictly on the attached documents,

if a figure needed for a calculation is missing, state “Insufficient Data to Calculate” rather than estimating.

Scenario B: Drafting an Investor Update From Raw Numbers

Read more here: https://aiportalen.substack.com/p/claude-for-financial-analysis

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