3 Hidden AI Tricks That Even Most AI Experts Don’t Know (2025 Edition)
🔍 Quick Summary – What you’ll get from this post
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 🔥
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:
⭐ 2. Advanced Mode — 2-Layer Decision Engine (PRO Version)
- 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)
🔍 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)
- 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.
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
🔁 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?”
- 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. 😄🤝

Thank you, yes exactly this is what I have been searching internet for a long time.
Thank You Jaydev, Glad you like it.