The 5 AI Prompts I Use To Analyse Stocks Before Investing

Whenever I analyse a stock, I do not start with the share price.

I start with the business.

Because behind every ticker symbol is a real company, with real customers, real competitors, real risks, and real numbers that we need to understand.

Today, with AI tools, stock research can become much faster.

But I think the quality of the output still depends on the quality of the prompt.

If we ask vague questions, we may get vague answers.

If we ask better prompts, we can use AI to organise information, challenge our thinking, and help us understand a company more clearly.

Over the years, I have built a simple five-prompt framework that I use when analysing stocks.

Of course, these prompts are also the same questions I would ask as an investor.

The difference is that I now use AI to help me structure the research more efficiently.

It is not perfect.

It does not guarantee that I will make money.

And it definitely does not replace my own thinking.

But it helps me stay disciplined.

More importantly, it helps me think like a business owner rather than a stock trader.

These are the five AI prompts I use before deciding whether a company is worth analysing deeper.

AI Prompt 1: Understand The Company’s Business And How It Makes Money

The first AI prompt I usually start with is:

“Tell me more about the company’s business and how the company makes money.”

This is always my starting point.

Before I even look at revenue growth, profit margin, valuation, or share price, I want to understand the business.

What does the company sell?

Who are its customers?

How does it charge them?

Is the revenue recurring, transactional, cyclical, or one-off?

What actually drives the business?

This sounds very basic, but I think this is where many investors make their first mistake.

They buy the stock because the chart looks good.

They buy because someone online is talking about it.

They buy because the company operates in a hot industry.

But they do not really understand how the business makes money.

That is why this is the first prompt.

If AI cannot help me understand the business in simple language, or if I still cannot explain the business clearly after reading the answer, then I probably should not invest in it.

This prompt also helps me decide whether the company is within my circle of competence.

There are thousands of listed companies in the world.

I do not need to invest in all of them.

I only need to invest in the ones I can understand well enough.

Because if I do not understand the business, everything else becomes guesswork.

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AI Prompt 2: Review The Previous FY Performance And Current FY Outlook

The second AI prompt I use is:

“What is the previous FY performance, and what is the current FY outlook and growth?”

Once I understand how the company makes money, the next step is to understand whether the company is actually performing well.

This is where I look at the previous financial year's performance.

I want to know whether revenue is growing, whether margins are improving or declining, whether cash flow is strong, and whether the balance sheet is healthy.

But I do not just want the headline numbers.

I want to understand the reasons behind the numbers.

For example, if revenue grew, was it because of real organic demand?

Was it driven by price increases?

Was it helped by acquisitions?

Was it due to currency movement?

Was it a one-off boost that may not repeat?

Likewise, if profit declined, I want to know whether it is a temporary issue or a structural problem.

This is why the current financial year outlook is also important.

Past performance tells me what happened.

The outlook tells me what management expects to happen next.

A company may have performed well last year, but if growth is slowing, margins are under pressure, or management is guiding for a weaker year, I need to understand why.

On the other hand, a company may have a weak year because of temporary issues, but if the long-term growth runway remains intact, it may still be worth studying.

This second prompt helps me connect the business model to the actual financial performance.

Because a good story must eventually show up in the numbers.

Want a concise version of Charlie Munger’s 25 human misjudgements? Download here!

AI Prompt 3: Analyse The Company’s Economic Moat

The third AI prompt I use is:

“Analyse the company’s economic moat in terms of 1) intangible assets, 2) cost advantage, 3) efficient scale, 4) network effect, and 5) switching cost.”

After understanding the business and its financial performance, I want to know whether the company has a durable competitive advantage.

To me, this is one of the most important parts of long-term investing.

A company can grow fast for a few years.

A company can report strong profits today.

But the real question is whether it can protect those profits from competitors over time.

That is where the economic moat comes in.

I usually break the moat down into five areas.

Intangible Assets

This can include brand, patents, licences, regulatory approvals, proprietary technology, data, or reputation.

A company with strong intangible assets may be difficult to compete against because competitors cannot easily replicate what it has built.

Cost Advantage

Can the company operate at a lower cost than its competitors?

If it can, it may be able to earn better margins or price more aggressively while still remaining profitable.

Efficient Scale

Sometimes, a market is only large enough for a few strong players.

When that happens, new competitors may not have enough incentive to enter because the economics are not attractive.

Network Effect

This happens when a product or platform becomes more valuable as more users, customers, partners, or participants join.

The stronger the network effect, the harder it may be for competitors to pull users away.

Switching Cost

This is one of my favourite moat sources.

If customers find it painful, costly, risky, or inconvenient to switch to another provider, the company may enjoy more stable revenue and stronger customer retention.

The reason I use this as the third prompt is simple.

Once I know what the company does and how it is performing, I want to know whether that performance can last.

Without a moat, high returns can attract competition.

And when competition comes in, margins can compress, growth can slow, and the investment thesis can weaken.

A strong moat does not make a company risk-free.

But it gives the company a better chance of staying relevant and profitable over the long run.

AI Prompt 4: Identify The Key Risks And How The Company Is Addressing Them

The fourth AI prompt I use is:

“What are the risks of investing in this company, and how is the company addressing these risks?”

After studying the moat, I force myself to look at the other side.

What can go wrong?

This is important because every investment has risks.

There is no such thing as a perfect company.

Even high-quality businesses can face regulatory pressure, technological disruption, poor capital allocation, customer concentration, weak execution, or changing consumer behaviour.

So instead of avoiding risks, I try to understand them.

Some risks are temporary.

Some risks are structural.

Some risks are manageable.

Some risks can completely break the investment thesis.

The key is to know the difference.

For example, if a company is facing temporary margin pressure due to investment in future growth, that may not be a big issue.

But if the company is losing customers because its product is no longer competitive, that is a very different problem.

I also want AI to help me organise how management is addressing these risks.

Are they investing in the right areas?

Are they diversifying revenue?

Are they strengthening the balance sheet?

Are they improving operations?

This prompt helps me stay balanced.

It is very easy to fall in love with a stock after reading about its growth, moat, and potential.

But investing is not just about upside.

It is also about protecting downside.

A good investment thesis should not only explain why the company can do well.

It should also explain what can go wrong and why the risks are still acceptable.

AI Prompt 5: Decide What Metrics To Track Going Forward

The fifth AI prompt I use is:

“As an analyst and an investor, what metrics should I keep a lookout for?”

This is the prompt that helps me monitor the company after analysing it.

Different businesses need to be tracked differently.

For a payment company, I may track processed volume, take rate, revenue growth, and EBITDA margin.

For a healthcare provider, I may track treatment volume, profit per treatment, operating margin, and debt levels.

For a software company, I may track recurring revenue, retention rate, organic growth, and cash conversion.

The mistake many investors make is that they only look at whether the share price goes up or down.

But share price movement alone does not tell me whether the business is improving.

I want to know whether the underlying business is progressing according to my thesis.

That is why identifying the right metrics is so important.

It gives me a scoreboard.

When quarterly or full-year results are released, I do not want to be distracted by headlines.

I want to know whether the key drivers of the business are moving in the right direction.

If the metrics improve, the thesis may be strengthened.

If the metrics weaken, I need to reassess.

If the original reason I was interested in the stock is no longer valid, I need to be honest with myself.

This final prompt turns the investment thesis into a monitoring system.

It helps me decide whether to add, hold, reduce, or exit.

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Why I Use These AI Stock Analysis Prompts In This Order

The order matters.

I do not start with valuation because valuation without business understanding can be dangerous.

A stock may look cheap, but if the business is poor, it may stay cheap for a reason.

I also do not start with the moat immediately because I first need to understand what the business actually does.

So the sequence is intentional.

First, I use AI to understand the business.

Then I use AI to review the company’s financial performance and outlook.

Next, I use AI to assess whether the company has a durable competitive advantage.

After that, I use AI to identify the key risks and how management is addressing them.

Finally, I use AI to determine the metrics I should track going forward.

To me, this is a logical flow.

The business model tells me what the company does.

Financial performance tells me whether it is doing well.

Economic moat tells me whether the performance can last.

Risk analysis tells me what can go wrong.

Key metrics tell me what to monitor after investing.

This framework keeps me grounded.

It prevents me from being overly excited by a good story.

It also prevents me from being overly fearful when a company faces temporary issues.

Most importantly, it helps me think like an owner.

Because when I buy a stock, I am not just buying a ticker.

I am buying a piece of a business.

What Happens After These Five AI Prompts?

These five AI prompts are not meant to be the full analysis.

They are my first filter.

They help me understand whether I should dive deeper and further analyse the company.

Because not every company deserves my time.

If the business is too difficult to understand, the performance is weak, the moat is unclear, or the risks are too difficult to manage, I may stop there.

But if the company passes these five prompts, that is when I go deeper into the financials.

This includes studying its revenue growth, profit, margins, cash flow, debt level, and balance sheet strength.

I will also look at return metrics such as ROE and ROIC.

Once I am satisfied with the business quality, financial performance, moat, risks, and key metrics, I will move on to valuation.

This is where I may use a discounted cash flow (DCF) model to estimate the company's intrinsic value.

But valuation should come after understanding the business, not before.

Because a DCF is only as useful as the assumptions behind it.

And those assumptions should be built on business understanding, not guesswork.

I will share more about how I think about valuation and DCF in the next blog.

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Final Thoughts On Using AI Prompts For Stock Analysis

AI can make stock research faster.

But it should not make us lazy.

For me, AI is not there to replace my thinking.

It is there to sharpen my thinking.

These five AI prompts help me slow down, organise my analysis, and understand whether a company deserves deeper research.

What does the company do?

How does it make money?

Is it performing well?

Does it have a moat?

What are the risks?

And what should I track going forward?

These prompts will not make every investment successful.

But they help me avoid many unnecessary mistakes.

They help me understand what I am buying.

And they help me build conviction based on business fundamentals, not market noise.

That is the kind of investing I prefer.

Patient.

Business-focused.

Risk-aware.

And grounded in the simple idea that behind every stock is a real company that must continue to create value over time.