3 Hidden AI Tricks That Even Most AI Experts Don’t Know (2025 Edition)

🔍 Quick Summary – What you’ll get from this post

No boring advice like “write better prompts” – only practical methods

Ways to make AI think with you, not just answer you

Simple examples you can try immediately

And a small challenge at the end 😉

I’ll be honest with you — I wasn’t always good at using AI.

When ChatGPT first came out, I did exactly what everyone else was doing: ask one question → get one answer → close the tab… and then complain, “Hmm, AI doesn’t understand me properly.” Sounds familiar, right?

One day I realised something crazy: AI wasn’t the problem. The way we use AI was the problem.

I started noticing a pattern. Everyone on the internet was giving the same type of “tips”:

“Give AI a role…”
“Write long prompts…”
“Be descriptive…”

Yeah bro, we all know that. And it still doesn’t give the kind of mind-blowing results we expect.

So I went down a rabbit hole.

I tested AI for planning, decision-making, writing, analysing, explaining, creating systems, predicting mistakes, even simulating situations.

And slowly, I discovered a few AI behaviours that nobody talks about publicly. Not YouTubers. Not bloggers. Not even “AI experts.”

These weren’t just tips. These were Hidden AI Tricks — little techniques that unlock a completely different level of output. The kind of tricks that make you say: “Wait… AI can do THIS too?”

And that’s why you’re here.

Maybe you’ve tried using AI. Maybe you’ve followed the generic advice online. And still felt like the output is… just okay. Not bad. But not exactly impressive either.

This post is for you if you want AI to actually save your time, think with you, and give you results that feel professional, original and useful.

I’m sharing the exact real-world tricks I learned through months of experimentation — the ones that changed how I use AI every single day.

So grab a cup of chai/coffee… and let’s dive into the part of AI nobody shows you 🔥

Table of Contents

1. Decision Engine – Turn AI Into a Smart Chooser (Not Just a Writer)

What this actually means

Most people use AI like this:

“Which side hustle is best for students?”
→ Random generic answer.

Decision Engine = you force AI to think like a comparison machine:

List all options
Create clear criteria
Score each option
Explain why it scored like that
Then give you a final decision

So instead of “one random suggestion”, you get:

A ranked list
A scoring table
A clear reason why option A is better than B for your situation

You’re basically telling AI:

“Don’t just answer. Judge, compare, and choose.”

Real example: choosing one idea to work on

Say you’re confused between 4 ideas:

Start a blog
Start a YouTube channel
Sell crochet products online
Start a micro-earning / AI tricks newsletter

You can tell AI:

“Behave like a decision engine and help me choose one main focus.”

You give it criteria like:

Time required per week
Money potential
Competition level
Skill match with me
How fast I can see first ₹1,000

Then AI doesn’t just say:

“Start a YouTube channel.”

Instead, it gives you something like:

A table (each idea vs each criterion)
Scores (1–10)
Short explanation why it gave that score
Final recommendation: “Start with X, keep Y as backup.”

Now the answer feels like a consultant, not a “chatbot”.

Problems users usually face with this trick

Even if they try something similar, people mess it up in a few ways:

1. Vague criteria

They say: “Which is best for me?”
But they don’t tell AI what “best” means (money? free time? low risk?)
➝ Result: AI gives a very safe, generic answer.

2. Too many options, no filtering

They dump 15–20 ideas and ask: “Which is best?”
➝ AI is forced to stay shallow for each option.

3. No numbers, only words

AI gives only paragraphs, no scoring or ranking.
➝ You still feel confused: “Okay, but which one is actually better?”

4. User doesn’t give personal context

If AI doesn’t know your skills, time, money, it will assume a generic person.
➝ The decision may not fit your situation.

5. Treating the first answer as final truth

They never ask AI to re-check or adjust weights.
➝ One-shot answer, no refinement = weaker decision.

Hidden upgrade: 2-Layer Decision Engine (the mini trick)

Here’s the secret extra trick to make this mode 2x better:

🔒 Layer 1: Criteria Builder
🔓 Layer 2: Decision Engine

Instead of jumping directly into “choose one”, you first tell AI:

“Before giving me any decision, ask me questions and help me define the criteria and their importance (weights). Only after that, run the decision engine.”

So AI first talks to you like this:

“What matters more to you right now – money or free time?”
“Are you okay to invest some money upfront?”
“Do you want something you can do anonymously or with your real name?”

From your answers, it creates:

Criteria list
Each with a weight (High / Medium / Low, or 1–5)

Then using that, it runs the comparison.

This way:

The result feels custom-made
You understand why it chose that option
And if you don’t like the outcome, you just say:

“Give more weight to [X], re-run the decision.”

Now AI becomes a tunable advisor, not a one-shot oracle.

⭐ 1. Fast Mode — Simple Decision Engine Prompt

Just paste this directly into ChatGPT:

🎯 PROMPT: Simple Decision Engine

I want you to act as a Decision Engine.  
Your job is to compare all my options using structured analysis and then choose the best one.

Follow these steps:

1. List all the options I give you.
2. Create a comparison table using these criteria:
   – Money potential
   – Time required per week
   – Learning curve
   – Competition level
   – Long-term growth
   – How well it fits my skills
3. Score each option from 1–10 for each criterion.
4. Write 1–2 lines explaining every score.
5. Add a weighted total score.
6. Finally, give me:
   – The best option
   – The second-best option
   – A short explanation of “Why this is the best choice for you.”

Ask me: “What options do you want to compare?” and wait for my answer.
  

⭐ 2. Advanced Mode — 2-Layer Decision Engine (PRO Version)

🎯 PROMPT: 2-Layer Decision Engine (Advanced)

I want you to act as a 2-Layer Decision Engine.  
Your job is to help me choose the best option with maximum accuracy.

——————–
LAYER 1 — CRITERIA BUILDER
——————–
Before giving me any recommendation:
1. Ask me 5–7 questions to understand my situation, goals, constraints, skill level, and preferences.
2. Using my answers, create:
   – A list of decision criteria
   – A weight for each criterion (High/Medium/Low or 1–5)
3. Show me the final criteria list and ask:
   “Can I proceed with the Decision Engine?”

Wait for my confirmation.

——————–
LAYER 2 — DECISION ENGINE
——————–
After I confirm:
1. List all my options.
2. Create a comparison table:
   – Rows = options
   – Columns = criteria + weights
3. Score each option from 1–10 for every criterion.
4. Multiply score × weight and calculate total weighted score.
5. Rank the options from best to worst.
6. Give me:
   – Winner option
   – Runner-up option
   – Why the winner matches my constraints
   – A scenario where the runner-up might actually be better

——————–
IMPORTANT
——————–
Always show:
– A decision table
– Weighted scoring
– A final summary
– A “If your priorities change…” alternative decision

Now ask me:
“What options do you want to compare?”
  

🔍 Quick Summary – Decision Engine Trick

Turns AI from a “reply machine” into a structured comparison engine
Works best when you’re stuck between 3–6 options (business ideas, side hustles, niches, etc.)
The real power comes from clear criteria + scoring + weights, making the final choice feel intentional

2. Blindfold Prompting — Let AI Engineer the Prompt Instead of You (Insanely Powerful)

What this actually means

Most people already give very specific prompts like:

“Write a 1500-word blog post about Task Mate with real examples and SEO keywords.”

And yet the output feels flat, predictable, and similar to everyone else’s content. This happens because you are deciding the prompt, not the AI.

You don’t always know which:

angle is most engaging
structure works best
tone fits your audience
workflow produces the strongest result

Blindfold Prompting flips the entire workflow — AI becomes the prompt designer, strategy builder, and execution engine.

Real example: Creating a blog post that doesn’t feel AI-generated

Suppose your audience is:

18–30, Indian students
want to earn online
hate robotic tone
prefer short, real-world explanations

Normally you’d write:

“Write a blog about hidden AI tricks for earning money.”

Blindfold Prompting instead makes AI generate:

Strategic content angles
A full 5-step prompt workflow (hook → story → examples → FAQ → CTA)
A 150-word sample so you can test the tone
An editorial critique of that sample

This avoids the #1 mistake: starting with the wrong prompt.

Problems users face even when they try Blindfold Prompting

They assume their first prompt is “good enough.”
They don’t give constraints (free tools only, low budget, no jargon).
They skip the sample test and jump straight to the full article.
They never ask AI to compare angles or workflows.
They treat AI as a writer instead of a strategist.

Hidden Upgrade: Multi-Layer Prompt Testing (The Real Power Move)

This technique multiplies Blindfold Prompting’s power:

AI designs 3–5 prompts with different strategies.
AI writes a short 150-word sample for each.
AI critiques its own samples and recommends the best one.

This produces output that feels self-audited and hyper-personalized.

⭐ Blindfold Prompting — Main Prompt (Prompt Architect Mode)

I want you to act as my Prompt Architect, not a content generator.

My goal:
<describe your goal in 1–2 lines>

My audience:
<country, age group, pain points, preferences>

Your tasks:
1. Ask me 5–7 focused questions to understand tone, purpose, depth, constraints, and preferred angle.
2. Based on my answers, design:
   – 3 strategic content angles
   – A 5-step prompt workflow for each angle
   – The purpose and expected style of each prompt
3. Choose the angle + workflow that YOU believe will work best for my audience.
   Explain your reasoning.
4. Generate a 150-word sample using your chosen workflow.
   Do NOT write the full content yet.
5. Critique the sample as a senior editor and propose one improvement.

Wait for my confirmation before generating the final content.
  

🔍 What the Meta Prompt Actually Does (The Secret Second Layer)

The Meta Prompt acts like a quality auditor that evaluates the Blindfold Prompt itself. Your first prompt is rarely the strongest version — so the Meta Prompt:

analyses your Blindfold Prompt for weaknesses and missing details
adds deeper constraints and personalization based on your audience
strengthens the structure and clarity of the prompt
rewrites a sharper, more powerful version of the Blindfold Prompt
creates two improved versions: one creative and one structured
lets you choose which one to lock in as your final optimized prompt

This is prompt-engineering layered on top of prompt-engineering — the step that makes Blindfold Prompting truly elite.

⭐ Blindfold Prompting — Meta Prompt (The Extra Hidden Trick)

Now act as a Meta-Prompt Engineer.

Your job is to evaluate the Blindfold Prompt I just used and improve it.

Do the following:
1. Identify weaknesses, missing constraints, unclear goals, or unused audience insights.
2. Suggest improvements that would produce even sharper, more personalized results.
3. Rewrite a stronger version of the Blindfold Prompt for my specific goal and audience.
4. Then generate one extreme version (creative/risky) and one safe version (structured/controlled).
5. Ask me which version I want to lock in as my final Blindfold Prompt.

Begin by critiquing the Blindfold Prompt I used earlier.
  

🔍 Quick Summary – Decision Engine Trick

Turns AI from a “reply machine” into a structured comparison engine
Works best when you’re stuck between 3–6 options (business ideas, side hustles, niches, etc.)
The real power comes from clear criteria + scoring + weights, making the final decision feel intentional

3. Triangulation Method — Force AI To Give You Answers It Normally Can’t

What this actually means

AI gives its best guess based on pattern-matching. That means:

sometimes it’s slightly wrong
sometimes it’s overconfident
sometimes it hides uncertainty
sometimes it avoids giving direct opinions
sometimes it gives a “middle” safe answer

The Triangulation Method solves this by forcing AI to look at:

multiple angles
multiple mental models
multiple personas
multiple constraints

…and then combine (triangulate) those answers into one truth-calibrated output.

In simple words:

Triangulation = Ask the same question in 3 different ways and combine all three answers for a higher-accuracy result.

This triggers a hidden internal behaviour: AI cross-checks its own reasoning. It is massively superior to normal one-shot prompting.

The real “trick” behind Triangulation (not the YouTube version)

Triangulation is NOT:

“Ask the AI from 3 perspectives”
“Use different tones”
“Ask it thrice”

Those are baby versions.

The real trick is:

You force AI to break its own prediction limits by giving it three contradictory cognitive roles and then make it resolve the contradictions.

This triggers a deeper internal reasoning layer called cross-context pattern resolution — something AI normally hides.

AI isn’t built to “think”, it’s built to “predict”. When you give three roles that conflict with each other, AI must:

build three separate reasoning chains
compare them
identify inconsistencies
merge them
find a solution that survives all three constraints

This is the closest you can get to “logical truth mode” without chain-of-thought access.

Real-world case: Choosing between 3 earning ideas with only 2 hours a day

Let’s take a very specific, practical scenario.

A 20-year-old commerce student wants to earn money and has:

2 hours per day
₹0 investment
Average English
Smartphone only
No video confidence
Needs the first ₹1,000 quickly

He is confused between:

Micro-earning apps
Blogging
Freelancing

A normal prompt gives generic advice like:

“Try freelancing if you have skills.”

Not very helpful.

The Triangulation Method transforms this decision completely.

Perspective A — Data Analyst (cold logic, numbers only)

Micro-earning apps = fastest payout but low earning ceiling
Blogging = slow start (2–4 months), requires research time
Freelancing = competitive but scalable
Student has 2 hours → blogging becomes too slow
Zero investment → micro-earning is viable, freelancing possible only with basic skills
Goal: earn first ₹1,000 → apps beat blogging/freelancing

Analyst Conclusion: Start with micro-earning to get ₹1,000. Parallelly upskill for freelancing.

Perspective B — Behavioral Psychologist (habits & motivation)

Students lose consistency when results are slow
Blogging = high dropout because no early reward
Freelancing = rejection-heavy → kills confidence
Micro-earning = small dopamine hits → increases motivation
Student lacks English confidence → blogging/freelancing frustration will spike

Psychologist Conclusion: Micro-earning gives momentum, prevents burnout, and builds discipline.

Perspective C — Risk–Reward Strategist (future payoff vs safety)

Micro-earning = safe, but no long-term growth
Freelancing = high growth but high initial rejection
Blogging = compounding growth but painfully slow
Best strategy: start with safe earnings → transition to scalable source within 30–60 days

Risk–Reward Conclusion: Use apps as a short-term cash engine → build freelance skills → then move to blogging later.

The real magic: Contradiction resolution

Here’s where A, B, and C collide.

Contradictions:

Analyst says: freelancing is scalable
Psychologist says: freelancing kills motivation early
Strategist says: freelancing is long-term only after small wins

Overlaps:

All three say micro-earning works for early income
All three agree blogging is too slow for 2 hours/day
All three agree freelancing is future-ready but not “day one”

Unified Output:

Start with micro-earning apps for 2–3 weeks to get your first ₹1,000 and build confidence. In parallel, learn ONE freelance skill (for example: image editing, Canva design, or caption writing). Take 3–5 small freelance tasks once you’re ready. Only later, when your skill and time increase, move into blogging for long-term growth.

🔥 Insider Tip – Triangulation Trick

Run the same Triangulation prompt twice with slightly different wording, then ask AI to compare both results and fix contradictions. This forces a deeper self-correction layer inside the model.

The trick inside the trick (how to use Triangulation properly)

Triangulation works only if you:

Create cognitive conflict by giving AI roles that will disagree.
Block repetition so each perspective builds a different reasoning chain.
Force contradiction resolution instead of accepting three parallel answers.
Force synthesis into one final strategy, not three separate ones.
Ask for an alternative path for people whose priorities are different.

⭐ Triangulation Method — Signature Prompt

I want you to perform Triangulated Reasoning.

My goal:
<describe your problem in 1–2 lines>

Your tasks:

1. Answer the question from three distinct expert perspectives:
   – Perspective A: Data Analyst (logic, numbers, trends)
   – Perspective B: Behavioral Psychologist (motivation, habits, mindset)
   – Perspective C: Risk-Reward Strategist (safety, payoff, long-term upside)

2. Each perspective must:
   – use a different mental model
   – give 3–5 insights
   – avoid repeating points from other perspectives

3. After giving all three perspectives, perform:
   – Contradiction Analysis (where do they disagree?)
   – Overlap Analysis (where do they align?)
   – Synthesis (merge everything into one unified answer)

4. Finally, give me:
   – The most reliable conclusion
   – One alternative path if my priorities change

Ask me:
“What is the problem you want to triangulate?”
  

🔁 Bonus Layer – Triangulation Deep Audit (Stress-Test Your Answer)

After you get a triangulated answer, you can add a second layer called the Triangulation Deep Audit. This meta step asks AI to attack its own recommendation, expose hidden assumptions, test different scenarios, and then upgrade the plan with safeguards and backup paths.

In simple words: Layer 1 asks “What seems right?”, Layer 2 asks “Will this still hold up when reality hits?”

Now act as a Triangulation Deep Auditor.

Take the unified answer you just gave me and stress-test it.

1. List the key assumptions behind each of the three perspectives and behind the final unified plan.
2. For each assumption, explain:
   – What if this assumption turns out to be wrong?
   – How badly would it affect the outcome?
3. Create 3 scenarios:
   – Best case (everything goes right)
   – Realistic case
   – Worst case (multiple things go wrong)
4. For each scenario:
   – Show how the current plan performs
   – Point out where it breaks or becomes weak
5. Upgrade the original plan by:
   – Adding safeguards and adjustments
   – Suggesting 1–2 backup options if the main path fails
6. Finally, give me:
   – A “robust version” of the plan (after your audit)
   – A simple decision rule like: “If X happens, switch to Y.”

Begin by listing the assumptions in your previous answer.
  

🔍 Quick Summary – Triangulation + Deep Audit

Uses three expert perspectives to build a more truthful, reliable answer
AI resolves contradictions and merges everything into one practical plan
The Deep Audit layer stress-tests the plan and adds safeguards + alternatives

Final Thoughts – Before You Close This Tab

Honestly, this whole post came from one simple thought — “Why are people still using AI like it’s Google search?”

Every day I see the same thing: good people, good ideas, but horrible AI output… not because they’re doing something wrong, but because nobody ever shows the real, high-leverage tricks that actually make a difference.

I wrote this guide so you don’t waste months like I did — jumping from random tips to random prompts. If you’ve come this far, I’m guessing you actually want to use AI properly, not just for fun or curiosity.

And that’s exactly why I’m doing something a bit different:

If this post gets 25 genuine comments (just share your thoughts, experience, or which trick you want to try first), I’ll send everyone a free PDF containing all the remaining hidden AI tricks that I didn’t publish here.

Why 25 comments?

Because I want this to go to the people who actually read, learn, and engage — not mindless traffic. If even 25 of us learn these techniques properly, that’s a solid start.

So yeah… drop a comment, ask something, argue with something, or just write “I’m in.”

Once the comment count hits 25, I’ll update the post with the link to the PDF for everyone — completely free.

Thanks for staying till the end. Now go apply these advanced AI prompting tricks and break AI in smart ways. 😄🤝

Frequently Asked Questions (AI Tricks)

Why do normal prompts fail even when I write long, detailed instructions?

Because length isn’t the problem — structure is. If your prompt doesn’t give criteria, constraints, roles, or reasoning steps, AI gives safe, generic answers. These tricks force AI to change how it thinks, not just how much it writes.

Can beginners use these hidden AI tricks without technical skills?

Absolutely. Everything here is designed for everyday users. You don’t need coding or AI knowledge — just follow the step-by-step structures and you’ll see results instantly.

Are these tricks only for ChatGPT, or work on Gemini, Claude too?

All tricks work on every major AI model: ChatGPT, Gemini, Claude, Copilot — all of them. These are model-agnostic reasoning upgrades, not hacks.

Will these tricks help in earning ideas, blogging or studying?

Yes — these tricks were built from real-use cases like content creation, niche research, decision-making, micro-earning strategies, productivity planning and more. They are not theory — they work daily.

Why didn’t you include all 10 AI tricks in this post?

Because the post becomes too long for mobile readers. Some tricks are more advanced and suitable only for people who engage deeply. That’s why the complete list will be shared via PDF once the post hits 25 comments.

How do I know if I am using AI in the wrong way?

Signs you’re using AI wrong: – Every answer sounds generic – Repetitive responses – Weak insights – You fix too much manually – AI never surprises you These tricks fix exactly this.

How many tricks should I start with?

Start with just one: Decision Engine or Triangulation. After feeling the quality jump, move to Blindfold Prompting. No need to learn everything in a day — these stack gradually.

Can I use these tricks to improve my earning methods or side hustles?

100%. Decision Engine helps you pick profitable ideas. Triangulation gives deeper, bias-free conclusions. Perfect for students, freelancers, and micro-earners.

Will this post get updated when new AI tricks come out?

Yes. AI evolves fast, and I update TakaTricks regularly with new practical techniques based on daily real-world experiments.

How do I get the free PDF of the remaining hidden AI tricks?

Just leave a genuine comment below. Once the post reaches 25 comments, I will update the article with the PDF link — free for everyone who participated.