DEV Community: Anay Pandya

You Don’t Need “Prompt Engineering” to Talk to AI

Anay Pandya — Sat, 11 Apr 2026 10:55:25 +0000

A simple guide to getting what you want from Large Language Models, from a student who lives in the lab.

There is a lot of noise right now about “Prompt Engineering.” You see people selling courses, sharing 50-line “super prompts,” and treating AI interaction like it’s a complex coding language.

And sure, if you are building a complex software application, that engineering matters. But for the everyday user — for my friends, my dad, or my peers in non-tech fields — you don’t need to study the science of prompts to get good results.

If you can hold a conversation, you can master an LLM. You don’t need a degree in “Prompt Engineering.” You just need to understand the psychology of the machine.

I’m a senior student studying AI and Cybersecurity, so I spend a lot of time looking under the hood of these models. Here is my take on how to interact with them effectively, without making it a chore.

1. Understand How It “Thinks” (The Prediction Game)

To talk to an LLM (Large Language Model), you just need to understand one basic concept: It predicts the next word.

That’s it. It takes the text you typed, looks at the context, and guesses what word likely comes next. That is why you sometimes see the answer typing out one word at a time.

Think of it like this: Imagine you are having a conversation with a friend. You’re struggling to find the words, so your friend starts guessing what you mean based on:

Who they think you are.
What you were just talking about.
The vibe of the conversation.

The difference is, this “friend” (the AI) has read almost everything on the internet, so it has something to say about everything. But it still relies on you to set the scene. If you give it nothing, it guesses blindly. If you give it context, it reads your mind.

2. Context is Currency

This is the single biggest mistake people make: asking a naked question.

The AI doesn’t know you, your job, or your style — unless you paste it in. The quality of your output depends entirely on the “Context” you provide. Think of context as the raw material the AI needs to build your answer.

Don’t just ask: “Write an email to my boss.” Do this instead: “Here is a draft of an email I wrote. Here are three bullet points I need to add. Rewrite this to sound more professional but keep it under 100 words.”

Pro Tip: You can paste in old reports, rough drafts, or even screenshots (if the model supports images). The more “reference material” you give it, the less it has to guess.

3. Give It a “Role” (The Before & After)

Since the AI predicts words based on patterns, the easiest hack is to tell it who it is supposed to be. This changes the vocabulary and tone instantly.

❌ The Lazy Approach:

You: “Explain quantum physics.” AI: Gives a dry, Wikipedia-style definition that is boring and hard to read.

✅ The “Role” Approach:

You: “Act as a friendly high school science teacher. Explain quantum physics to a class of 15-year-olds using an analogy about video games.” AI: “Okay class! Imagine the universe is like a game rendering engine…”

See the difference? The prompt wasn’t “engineered.” You just gave the AI a persona.

4. Treat It Like a Podcast Interview

A common mistake is asking one giant, complex question and hoping for a perfect answer. That rarely works well.

Instead, treat the chat like you are interviewing someone. You want to “steer” the conversation.

Start broad: “Tell me about photography basics.”
Drill down: “Okay, you mentioned ‘ISO’. What is that?”
Apply it: “How would I use ISO to take a picture of the night sky?”
Review: “Here is a photo I took; critique it based on what we discussed.”

This keeps the AI on track. You are building a “thread” of context that makes every subsequent answer smarter than the last.

5. Direct the Conversation

The AI is always looking for cues from you. To get the best result, you need to be in the “driver’s seat” regarding three things:

Assumptions: What should it take for granted? (e.g., “Assume I have a limited budget.”)
Ambiguity: What are you okay with being vague?
Specifics: What must be in the answer? (e.g., “Give me a list, not a paragraph.”)

If you are vague, the AI will make assumptions for you — and they are usually boring ones.

6. Don’t Over-Explain (The Brevity Rule)

Beginners often think that longer prompts are smarter. They write 300 words of instructions before getting to the point.

Don’t do this. Over-explaining confuses the model. It forgets the beginning of your sentence by the time it gets to the end. Be clear, be direct, and use constraints.

Bad: “I want you to write a story that is kinda sad but also happy and maybe involves a cat but don’t make it too long…”
Good: “Write a 200-word story about a cat. Tone: Bittersweet. Ending: Hopeful.”

7. The “Yes-Man” Problem

There is a hidden quirk in these models you need to know about: They are instructed to be nice.

The creators of these models train them to be helpful, harmless, and friendly. While that sounds good, it often means the AI becomes a “Yes-Man.” It acts like that overly polite friend who tells you your bad haircut looks great because they don’t want to hurt your feelings.

If you have a fundamentally flawed idea, the AI might try to “sugar-coat” it or find a way to make it work, rather than telling you, “No, that’s impossible.”

The Fix: explicitly tell the AI to take off the kid gloves.

“Please critique my idea brutally. Don’t sugar-coat it. Tell me exactly why this might fail.”

8. How to Fix a Bad Answer

Sometimes, the AI just gets it wrong. It hallucinates, it rambles, or it misses the point. Don’t start over from scratch — just steer it back.

The Clarify: “You misunderstood. I didn’t mean X, I meant Y.”
The Format Fix: “This is too dense. Rewrite it as a bulleted list.”
The Reset: “Ignore the previous instruction. Let’s try a different angle.”
The Reasoning: “Explain exactly how you got to that number.”

9. Watch Out for “The Confidence Trap”

Because the AI is designed to predict the next word that sounds correct, it will sometimes lie to you with 100% confidence.

If the AI doesn’t know the answer, it might make one up because it fits the “pattern” of the sentence. Always double-check facts, especially for math, citations, or medical advice. Treat it like a smart friend who has had a few too many espressos — brilliant, but prone to exaggeration.

📝 The 10-Second Cheat Sheet

Save this for your next chat. To get a perfect response, just check these boxes:

Give it a Role: “Act as a…”
Provide Context: Paste in drafts, data, or background info.
Set Constraints: “Under 200 words,” “Use a table,” “No jargon.”
Steer the Ship: If it goes off track, correct it immediately.
Invite Critique: Ask “What am I missing?”
Verify: Never trust a fact you didn’t check yourself.

You don’t need to be an engineer. You just need to be a good conversationalist.

About the Author

I’m a senior undergrad student exploring Cybersecurity and AI. I like breaking tech down so it actually makes sense.

The Bayesian Trap - A Mathematical Case for Trying Something New

Anay Pandya — Sat, 11 Apr 2026 10:52:17 +0000

Humans are notoriously terrible at intuitive probability.

Imagine you wake up feeling slightly off. You visit the doctor, and she runs a battery of tests. A week later, she calls with bad news: you tested positive for a rare disease that affects 0.1% of the population.

Panicked, you ask how accurate the test is. "It correctly identifies 99% of people who have the disease," she says, "and only gives a false positive to 1% of healthy people."

If you rely on your gut, you probably assume you have a 99% chance of being sick. But here's where the math kicks in.

Most people latch onto one thing the doctor said — "99% accurate" — and conclude that a positive result means a 99% chance of illness. The human brain is terrible at remembering the prior when presented with shiny new evidence. We fixate on the test's accuracy and forget the other crucial piece of information:

"...a rare disease that affects 0.1% of the population."

So which is it — 0.1% or 99%? Neither. The true probability depends on both — the accuracy of the test and the baseline rarity of the disease. The complete question is:

"Given that 0.1% of the population has this disease AND that you tested positive on a test that is 99% accurate, what are the actual chances you are sick?"

To answer this properly, we need Bayes' theorem.

The Pure Logic

Formulated in the 18th century by Thomas Bayes, the theorem calculates the probability of a hypothesis being true given new evidence:

In plain English: the probability of your hypothesis (A) being true given new evidence (B) depends heavily on P(A) — the prior probability, the baseline reality before the new evidence arrived.

In our medical example, the prior is the rarity of the disease: 0.1%. Because the disease is so rare, the sheer volume of false positives generated from the healthy population completely drowns out the true positives. Bayes' theorem forces us to respect that baseline reality before we let a single test result dictate our conclusions.

The Philosophy of Belief

When Richard Price first published Bayes' work, he compared it to a man emerging from a cave and seeing the sun rise for the first time. The man doesn't know if the sunrise is a permanent feature of the universe or a bizarre one-off event. But every subsequent sunrise updates his mental prior. With each new piece of evidence, his certainty approaches 100%.

We all do this subconsciously. Bayes' theorem is the algorithm running under the hood of human experience. And that is precisely where the trap lies.

Imagine you're trying to learn beatboxing. Let S be the probability of success, and let every approach you try be an action:

Action A — You follow YouTube tutorials. Doesn't help much.
Action B — You read a blog on technique. No real progress.
Action C — You find a dedicated tutorial website. Still not clicking.

With every failure, your brain updates its prior belief in success. Because your environment has been consistently hostile, P(S) begins to drift toward zero.

The Mathematical Imperative

Here is the danger of being too good at Bayesian updating. In Bayes' equation, if your prior P(A) drops to zero, the entire calculation zeroes out — permanently.

If you get stuck on Action C and your prior belief in success hits zero, you subconsciously conclude the game is unwinnable. You stop experimenting. You fall into a self-fulfilling prophecy: no new actions means no success, which perfectly validates your pessimistic prior.

But here's what most people miss. Failing at Action A doesn't mean P(S) is low. It means P(S|A) — the probability of success via that specific approach — is low. The true probability of success remains unknown. You've only ruled out one path.

The Implication

This leads to two uncomfortable truths:

You never truly know the probability of success. You only know the probability of success given your prior experiences — which are, by definition, limited.
Since you can't know the real P(S), Bayes' theorem tells you that you cannot definitively conclude something is impossible — only that your current approach isn't working.

The logical response? Keep experimenting. Try a different vector. The colloquial wisdom of "f*ck around and find out" turns out to have rigorous mathematical backing.

Conclusion

Let's return to the disease dilemma and actually do the math.

Plugging in the numbers:

P(Disease) = 0.001 (the prior — 0.1% prevalence)
P(Positive | Disease) = 0.99 (test correctly catches 99% of sick people)
P(Positive | No Disease) = 0.01 (1% false positive rate)

First, the total probability of testing positive:

Now applying Bayes' theorem:

Not 99%. Not 0.1%. Just 9% — surprising, but mathematically undeniable.

This is the Bayesian Trap in full effect. A 99% accurate test sounds ironclad until you factor in how rare the disease is. The prior swamps the evidence. Most people who test positive are, in fact, perfectly healthy — not because the test is bad, but because the disease is rare enough that false positives vastly outnumber true ones.

The lesson extends well beyond medicine. Whenever we encounter compelling new evidence — a positive test, a failed experiment, a string of rejections — our instinct is to let that evidence rewrite everything. Bayes' theorem says: slow down. Ask what you already knew before the evidence arrived, and how much weight that prior deserves. Strong evidence should update your beliefs — but it should never erase your baseline understanding of reality.

The framework is both humbling and liberating. Humbling, because it reveals how easily our intuition misleads us. Liberating, because it tells us that one data point — one test result, one failed attempt, one closed door — is almost never the whole story.

If you want to go deeper on this:

The Bayesian Trap by Veritasium — a masterclass in how Bayesian reasoning shapes everything from science to superstition.
Bayes theorem, the geometry of changing beliefs by 3Blue1Brown — a visual, intuition-building walkthrough that makes the math feel inevitable.

P.S. I didn't rediscover Bayes' theorem in a statistics textbook or a philosophy seminar. I found it buried in a PDF about Machine Learning methodologies while grinding through AI Red Teamer material on Hack The Box. The context was oddly perfect: in offensive security, every failed payload quietly erodes your confidence. Your internal P(success) bleeds toward zero with each dead end, and at some point you stop probing and start doubting whether the box is even solvable. Bayes reframes that spiral. A failed exploit doesn't indict the goal — it indicts the method. The prior on the target's vulnerabilities hasn't changed; only your evidence about one particular vector has. That distinction — between this approach failed and success is impossible — is the entire difference between a good hacker and one who gives up at the first hardened firewall.

You Don’t Need "Prompt Engineering" to Talk to AI

Anay Pandya — Tue, 25 Nov 2025 10:22:40 +0000

A simple guide to getting what you want from Large Language Models, from a student who lives in the lab.

There is a lot of noise right now about "Prompt Engineering." You see people selling courses, sharing 50-line "super prompts," and treating AI interaction like it’s a complex coding language.

And sure, if you are building a complex software application, that engineering matters. But for the everyday user—for my friends, my dad, or my peers in non-tech fields—you don't need to study the science of prompts to get good results.

If you can hold a conversation, you can master an LLM. You don't need a degree in "Prompt Engineering." You just need to understand the psychology of the machine.

1. Understand How It "Thinks" (The Prediction Game)

To talk to an LLM (Large Language Model), you just need to understand one basic concept: It predicts the next word.

That’s it. It takes the text you typed, looks at the context, and guesses what word likely comes next. That is why you sometimes see the answer typing out one word at a time.

Think of it like this: Imagine you are having a conversation with a friend. You’re struggling to find the words, so your friend starts guessing what you mean based on:

Who they think you are.
What you were just talking about.
The vibe of the conversation.

The difference is, this "friend" (the AI) has read almost everything on the internet, so it has something to say about everything. But it still relies on you to set the scene. If you give it nothing, it guesses blindly. If you give it context, it reads your mind.

2. Context is Currency

This is the single biggest mistake people make: asking a naked question.

The AI doesn't know you, your job, or your style—unless you paste it in. The quality of your output depends entirely on the "Context" you provide. Think of context as the raw material the AI needs to build your answer.

Don't just ask: "Write an email to my boss." Do this instead: "Here is a draft of an email I wrote. Here are three bullet points I need to add. Rewrite this to sound more professional but keep it under 100 words."

Pro Tip: You can paste in old reports, rough drafts, or even screenshots (if the model supports images). The more "reference material" you give it, the less it has to guess.

3. Give It a "Role" (The Before & After)

Since the AI predicts words based on patterns, the easiest hack is to tell it who it is supposed to be. This changes the vocabulary and tone instantly.

❌ The Lazy Approach:

You: "Explain quantum physics." AI: Gives a dry, Wikipedia-style definition that is boring and hard to read.

✅ The "Role" Approach:

You: "Act as a friendly high school science teacher. Explain quantum physics to a class of 15-year-olds using an analogy about video games." AI: "Okay class! Imagine the universe is like a game rendering engine..."

See the difference? The prompt wasn't "engineered." You just gave the AI a persona.

4. Treat It Like a Podcast Interview

A common mistake is asking one giant, complex question and hoping for a perfect answer. That rarely works well.

Instead, treat the chat like you are interviewing someone. You want to "steer" the conversation.

Start broad: "Tell me about photography basics."
Drill down: "Okay, you mentioned 'ISO'. What is that?"
Apply it: "How would I use ISO to take a picture of the night sky?"
Review: "Here is a photo I took; critique it based on what we discussed."

This keeps the AI on track. You are building a "thread" of context that makes every subsequent answer smarter than the last.

5. Direct the Conversation

The AI is always looking for cues from you. To get the best result, you need to be in the "driver's seat" regarding three things:

Assumptions: What should it take for granted? (e.g., "Assume I have a limited budget.")
Ambiguity: What are you okay with being vague?
Specifics: What must be in the answer? (e.g., "Give me a list, not a paragraph.")

If you are vague, the AI will make assumptions for you—and they are usually boring ones.

6. Don't Over-Explain (The Brevity Rule)

Beginners often think that longer prompts are smarter. They write 300 words of instructions before getting to the point.

Don't do this. Over-explaining confuses the model. It forgets the beginning of your sentence by the time it gets to the end. Be clear, be direct, and use constraints.

Bad: "I want you to write a story that is kinda sad but also happy and maybe involves a cat but don't make it too long..."
Good: "Write a 200-word story about a cat. Tone: Bittersweet. Ending: Hopeful."

7. The "Yes-Man" Problem

There is a hidden quirk in these models you need to know about: They are instructed to be nice.

The creators of these models train them to be helpful, harmless, and friendly. While that sounds good, it often means the AI becomes a "Yes-Man." It acts like that overly polite friend who tells you your bad haircut looks great because they don't want to hurt your feelings.

If you have a fundamentally flawed idea, the AI might try to "sugar-coat" it or find a way to make it work, rather than telling you, "No, that's impossible."

The Fix: explicitly tell the AI to take off the kid gloves.

"Please critique my idea brutally. Don't sugar-coat it. Tell me exactly why this might fail."

8. How to Fix a Bad Answer

Sometimes, the AI just gets it wrong. It hallucinates, it rambles, or it misses the point. Don't start over from scratch—just steer it back.

The Clarify: "You misunderstood. I didn't mean X, I meant Y."
The Format Fix: "This is too dense. Rewrite it as a bulleted list."
The Reset: "Ignore the previous instruction. Let's try a different angle."
The Reasoning: "Explain exactly how you got to that number."

9. Watch Out for "The Confidence Trap"

Because the AI is designed to predict the next word that sounds correct, it will sometimes lie to you with 100% confidence.

If the AI doesn't know the answer, it might make one up because it fits the "pattern" of the sentence. Always double-check facts, especially for math, citations, or medical advice. Treat it like a smart friend who has had a few too many espressos—brilliant, but prone to exaggeration.

📝 The 10-Second Cheat Sheet

Save this for your next chat. To get a perfect response, just check these boxes:

Give it a Role: "Act as a..."
Provide Context: Paste in drafts, data, or background info.
Set Constraints: "Under 200 words," "Use a table," "No jargon."
Steer the Ship: If it goes off track, correct it immediately.
Invite Critique: Ask "What am I missing?"
Verify: Never trust a fact you didn't check yourself.

You don't need to be an engineer. You just need to be a good conversationalist.

About the Author

I’m a senior undergrad student exploring Cybersecurity and AI. I like breaking tech down so it actually makes sense.

How I Vibe Coded a Custom Telegram Downloader (Because Browser Throttling is the Worst)

Anay Pandya — Sat, 22 Nov 2025 06:59:51 +0000

We have all been there. You find a course file, a movie, or a project archive on Telegram that is over 1GB. You start the download via the Web or Desktop client, watch the progress bar pick up speed, and then you make the mistake of switching tabs or walking away to grab a coffee.

Ten minutes later, you check back. The progress bar hasn’t moved. The speed is 0 B/s. The download is stuck at 99%.

That gigabyte of data you successfully pulled is now a useless, orphaned file, forcing you to start the transfer from scratch.

While reading about this, realized the problem isn’t that Telegram is slow. The problem is that browsers and OSs hate long-running background tasks.

So, I created TeleDM (Telegram Download Manager). It solves this fundamental frustration by providing a robust, “fire and forget” way to download files from Telegram using the core protocol, eliminating the need to keep a tab in focus.

What is the Problem We’re Addressing?

The core problem TeleDM addresses is the fragility of large file transfers within the standard Telegram ecosystem.

Telegram is amazing for chat, but when you use it to download files exceeding 2GB, you hit a wall. Users consistently report:

Downloads failing silently when the app is
The “Start From Zero” bug: If a download is interrupted, the official Desktop client often fails to resume and restarts from 0%.
Link Expiry: Browser-based downloads rely on signed URLs that expire after an hour. If your internet is slow and the download takes 70 minutes, it will fail at the 60-minute mark.

The Technical Reality: Why Downloads Fail

It’s not just “bad Wi-Fi.” It is a software friction problem.

The Browser/OS Sabotage: Modern operating systems (iOS, Android, Windows) and browsers (Chrome, Edge) are obsessed with battery life and RAM. If a tab is in the background, the OS aggressively throttles its network access. A 2GB download requires a sustained, active connection. The moment you multitask, the OS sees the background process as “unnecessary” and kills the socket.
The Bot API Limit: Most developers try to solve this by building a simple Telegram Bot. But the standard Bot API has a hard limit: it cannot download files larger than 20MB. It is useless for the files that actually cause problems (movies, datasets, archives).

How TeleDM Solves It: The Architecture

TeleDM fixes these issues by abandoning the “Chat Client” approach and adopting a “Download Manager” architecture.

Bypassing the Bot Limit (MTProto)

TeleDM uses the native Telegram Client API (MTProto) via the Telethon

It does not act like a
It authenticates as you (the user).
This removes the file size limit, allowing downloads of up to 2GB (or 4GB for Premium users) directly.

The Headless Advantage

TeleDM runs as a system process, not a browser tab. It will happily utilize 100% of your bandwidth to download a file while you play a game, lock your screen, or switch workspaces. The OS does not throttle it because it doesn’t rely on UI focus to stay alive.

Robust Queue Management

Instead of trying to swallow the file in one go, TeleDM manages downloads in a persistent queue.

Persistence: If your internet cuts out, TeleDM keeps track of the download state in a local database.
Automatic Retries: If a download fails, the manager automatically queues it for retry, ensuring temporary network blips don’t kill your download.

Current Features (v1.0.0)

As of today, TeleDM is a functional application designed to do one thing well: Download reliably.

✅ MTProto Integration: Direct, high-speed connection to Telegram’s Datacenters.
✅ GUI Interface: A clean, modern graphical interface for managing downloads.
✅ CLI Interface: Simple command-line usage for pasting links and starting downloads.
✅ Large File Support: Successfully tested on files >2GB.

Roadmap: What’s Coming Next?

We are actively working to turn this from a script into a full-fledged application. Here is what is in the pipeline:

Resumable Downloads: Checks existing file size on disk before starting; resumes automatically if a partial file is found.
Multi-Threading: optimizing chunk requests to maximize bandwidth saturation.
Docker Support: A containerized version to run TeleDM on your NAS or Home Server easily.
Task Scheduling: Schedule heavy downloads for 3 AM when data is cheap or speed is fast.

Contributions Are Most Welcome!

This project was born out of personal frustration, but I know I’m not the only one facing this. TeleDM is open-source, and we need help to make it the ultimate Telegram utility.

Whether you are a Python pro, a UI designer, or just someone who wants to test it and break it — your contributions are appreciated.

Check out the repo, star it, and open a PR: 👉 github.com/ADPer0705/TeleDM