"Alright, Underoos," Tony Stark began, leaning back in his lab chair, a holographic display shimmering before them. Peter Parker, perched on a stool, was trying to look attentive but kept glancing at a new suit prototype.

"Focus, kid. This isn't about new web-shooters, it's about how Jarvis processes all that intel. You know, the 'brains' behind the operation. We're talking about how an GenAI, like the ones that help me predict Loki's next move or analyze alien tech, 'understands' language. Think of it like this: every story, every report, every single word we feed it, needs to be broken down."

Jarvis as GPT : Explained by Tony Stark

Peter's brow furrowed. "Jarvis as... GPT? Mr. Stark, what's a GPT? Is it some new kind of arc reactor?"

Tony chuckled, adjusting his glasses. "Good question, Pete. GPT stands for Generative Pre-trained Transformer. And yeah, it's kind of like the arc reactor for Jarvis's brain – it powers his ability to understand and respond to the universe."

"Let's break it down:"

Generative: Crafting New Realities (and Precautionary Measures)

Generative. This means Jarvis doesn't just parrot back what I've told him. He can create new information, new solutions, new strategies. When we face a new threat, like a rogue Kree warship, Jarvis can analyze its energy signatures and generate blueprints for a counter-weapon.

He can also predict Ultron's escape routes or simulate alien invasion scenarios to generate optimal defensive strategies, from Iron Legion protocols to city evacuation routes. He anticipates and creates solutions for things that haven't even happened yet.

Pre-trained: The Avengers' Collective Knowledge

Pre-trained. Before Jarvis can 'generate' anything useful, he needs to know everything. I've fed him an insane amount of data – not just computer code and sensor readings, but every piece of information I've ever collected: multi-verse data, alien attack techniques from the Chitauri to the Skrulls, and all the Avengers' combat data. He's analyzed our fighting styles, strengths, and weaknesses, and knows how our enemies fight. It's like giving him the ultimate Marvel encyclopedia, but one that he's internalizing every single detail."

Transformer: The Brain of the Battle

Transformer. This is the core 'brain' architecture, the engine that makes sense of all that pre-trained data and allows him to generate new insights. It's like a hyper-efficient command center within Jarvis that processes data from different inputs – whether it's an energy spike from a new alien threat or a distress call. While we're being attacked, or are in danger, the Transformer part of Jarvis is constantly processing complex battlefield data, connecting the dots using multi-head attention, and formulating rapid responses. He can instantly suggest optimal power outputs for my suit or calculate precise web-shot trajectories to disarm a bomb. He transforms raw data into actionable intelligence, in real-time, when our lives depend on it."

Tony leaned forward, a glint in his eye. "So, GPT isn't just a fancy acronym, Pete. It's the architecture that allows Jarvis to be Jarvis – to not just assist, but to anticipate, innovate, and protect. It's why he's the best AI partner an Avenger could ask for."

Peter nodded, a new understanding dawning in his eyes. "So, he's like... the ultimate, super-smart, always-learning, battle-ready strategist that you built?"

"Exactly, kid," Tony grinned. "Now, about those new web-shooters..."

Researcher Tony Stark vs Developer Peter Parker in GenAI

Tony Stark: The Generative AI Researcher vs. Peter Parker: The Generative AI Developer

Now listen, Pete — here’s where it all comes together."

"I’m the Generative AI Researcher in this story. The architect behind Jarvis’s GPT-like brain. I’ve spent sleepless nights designing systems that can learn from every battle we’ve fought — from alien invasions to tech breakdowns to cosmic anomalies. My mission? To build the ultimate synthetic mind. One that can understand patterns, generate strategies, and evolve with every threat the universe throws at us. Whether it’s a new repulsor frequency or a counterattack for an enemy we haven’t even met yet — I’m feeding Jarvis that data. I’m pushing the boundaries of what AI can know, learn, and create."

"But you, Underoos? You’re the Generative AI Developer — the hands-on genius."

"You take that powerful brain I built and bring it to life. You’re the one wiring it into your web-shooters, your HUD, your suit's combat interface. You're writing the code that makes Jarvis useful on the frontlines — not just smart in a lab. You adapt this tech to real-world chaos: rooftops, alleyways, high-speed chases. While I design the intelligence, you give it purpose. Your creativity and your questions? They drive innovation. You’re not just using AI — you’re shaping how it evolves to save lives."

Let’s Dive Depper into Transformers Peter

Tokenization

First part is tokenization, what ever word you say to the jarvis like 'spider-man’,’peter parker’,’good neighbour’ , is a piece of information which include some part of information.

"Imagine I say 'Spider-Man' to GPT. It doesn’t go, 'Oh, the guy in red spandex!'
Instead, it chops it up into smaller parts — called tokens."

Examples:

• ‘Spider-Man’ → could be tokenized as → [‘Spider’, ‘-’, ‘Man’]

• Or even smaller → [‘Spi’, ‘der’, ‘-’, ‘Man’]

But for jarvis its just string of character doesnt have meaning to it so it tokenize the words or sentences into combination of characters or as a character and convert it into numbers which are already predefined according to the model.

Each model provides different tokenization method.

import tiktoken

enc=tiktoken.get_encoding("cl100k_base")

text="Hello everyone i am Vyom Singh"
result=enc.encode(text)
print(result)
print(enc.decode(result))

[9906, 5127, 602, 1097, 96100, 316, 27992]
Hello everyone i am Vyom Singh

These words are known as vocabs and size of collection of all these words are known as vocab-size. Bigger vocab size require more computing power to train and analyze the train model

Vector Embeddings

Iron Man turned to Peter and said:

“Alright, Peter — now comes the most interesting and crucial part: how GPT actually understands the meaning behind words.”

“Let’s say we’re all part of the Avengers — you, me, Hulk, Natasha, and Thor. But instead of grouping us by how we look, I want to categorize us based on our traits, personalities, and powers.”

“So, imagine plotting each of us as a point in a 3D space — where the coordinates represent things like strength, intelligence, agility, and so on. That’s what GPT does. It converts each word or in this case, each person into a vector: a list of numbers that captures key characteristics.”

Vector Embedding of Avengers

“So when someone tells JARVIS, ‘Hulk and Thor are strong’, GPT can understand the similarity because their vectors are close together in this trait-space.”

import google.generativeai as genai
from dotenv import load_dotenv
import os

load_dotenv()
api_key=os.getenv("GOOGLE_KEY")
genai.configure(api_key=api_key)

result=genai.embed_content(
    model="models/embedding-001",
    content="Hello everyone, I am Vyom Singh",
)
print(result["embedding"])

“But if someone says, ‘Hulk hacked my computer’, GPT knows something’s off. Why? Because Hulk’s vector is far from mine on the ‘techy’ axis — it doesn't fit the pattern GPT has learned.”

Positional Embedding

Peter do you think only understanding the meaning of each word is enought to understand the whole the sentence for the GPT.Peter replied after thinking deepely “yess, I think so”. tony interrupted no its not.

If person says “Thor hits Hulk” and “Hulk hits the Thor” both of the sentences have different meaning so positioning of words thus play a crucial role so here comes positional embedding for rescue.

What if all of us are standing in a line :

Hulk at 1

Iron Man (me) at 2

Spiderman (you) at 3

As gpt know each of us traits from vector embedding , but that not enough gpt also needs to know each of us position in the team lineup so it needs position vector to understand about the position of each character

Final Input to GPT = Vector Embedding + Positional Embedding

Here is the simple table to understand how its possible

Ironman continued “Otherwise, GPT would think ‘You saved the world, Spider-Man’ and ‘Spider-Man saved the world, you’ mean the same thing — and I’m not giving you credit for my missions, okay?"

Self-Attention

Peter smiled and said “I understood how GPT gets traits and position of words but how it decides which words matter the most!”

Iron man replied “Great question Pete, lets dive deeper into it .

Imagine you , me, hulk,Thor and black widow we all are in a team of special mission by the avengers. So before attacking we go through each others positions what each other are doing, feeling so we can act in a best possible way this is Self attention.

Each Avenger pays attention to every other Avenger to understand the full context of the team same in gpt each word pays attention to very word in the input understand the aspect of the whole input.

Thus we have different ways of doing that :

Single-Attention

Imagine it’s a battle, and you're Spider-Man. You need to decide whom to focus on — is Hulk smashing the most enemies? Is Black Widow sneaking behind enemy lines? You choose one strategy to focus your attention.

Same in case of GPT it only analyse the sentence through single persecptive-one realationship.
It's like using just one type of lens to decide what matters most: maybe distance, or strength, or position — but just one at a time.

Let’s assume Spiderman want to coodinate focusing on the one aspect(Strength):

So you spiddy will give more attention to Hulk as you focusing from single lens of Strength.

Multi-Attention

But GPT my friend does’nt work liket that it uses Multi-Head-Attention.

In the real Transformer models, we use Multi-Head Attention. That’s like Spider-Man analyzing the battle through multiple strategies at once — strength, speed, communication, teamwork, distance — all in parallel.

In GPT each head is a seperate attention which focus on each words from particular lens, so different head sees with differnt lens or perspective and then we combine them all to get the fuller final result

Lets say you spiddy wants to use multi-head Attention and uses four differnt head each focusing on one single aspects

Peter replied excitedly “So multi-head attention is like using multiple brains at once, each seeing things differently — and then combining their insights?"

Tony said”Exactly. It’s what makes GPT smarter than your average AI sidekick. Or Avenger.”

Decoder / Output

And finally, Pete," Iron Man said, "comes the part everyone’s waiting for — the response from GPT... or let’s say, from Jarvis."

"Jarvis doesn’t just blurt out answers randomly. It looks at everything you've said — the entire context — and then uses all its past training: every alien battle we’ve fought, every strategy we’ve tried, every Avenger’s unique strength.

Based on that knowledge, it predicts the next best word or action — just like how I decide whether to call Thor or launch a drone strike."

Spiderman wondered, "How does it get trained?"

Phases of LLM (Large Language Model)

Training Phase

Tony replied “Peter, imagine we’re assembling the Avengers for the first time. You, me, Natasha, Hulk, and Thor have never worked together before. So what do we do first? We train."

In LLMs, training is like the bootcamp phase where the model is fed huge amounts of data (text from books, websites, articles, etc.) to learn patterns, context, grammar, logic, facts, etc.

I have fed Jarvis millions of mission reports, conversations, and research logs.

Every line like "Hulk smashes", "Thor wields Mjölnir", and "Natasha is stealthy" helps Jarvis learn associations between words.

Jarvis doesn’t know facts instantly. Instead, it learns by trial and error — just like you learning to be a hero. The model adjusts its weights (its memory of patterns) over time.

Every time Jarvis makes a prediction — whether it’s the next word or the next move — and it turns out wrong, it doesn’t just shrug it off.

Instead, it learns from the mistake by adjusting the internal weights — kind of like tweaking your Spidey-tech after a failed swing.

It backtracks, analyzes where things went off, and updates itself so next time, the prediction — or the attack — hits closer to the mark."

LLM Training = Avengers learning how to work as a team, analyzing past missions, and building intuition over time.

Inference Phase

"Now Peter, imagine you’re out on a mission, and you ask Jarvis for help. Jarvis doesn’t train again — it just uses what it already knows to help you instantly."

Inference is when the trained model is used to answer real questions or generate responses. It’s fast, efficient, and doesn’t change the model's knowledge — it just uses it.

During a mission, if you asks: “Jarvis, how do I disable a Stark drone?”

Jarvis uses what it already learned during training and replies: “Use an EMP burst followed by a magnetic disruptor.”

No learning happens here — just smart application of what was already learned.

LLM Inference = Jarvis helping Peter in real-time using everything it learned from years of Stark’s data and battle history.

Conclusion

Tony Stark explains to Peter Parker how Jarvis, powered by GPT (Generative Pre-trained Transformer), processes information to anticipate and respond to threats. He breaks down the components of GPT—Generative, Pre-trained, Transformer—to highlight Jarvis's capabilities in creating new insights from vast amounts of data. Stark and Parker discuss concepts like tokenization, vector and positional embeddings, self-attention, multi-head attention, and the phases of large language models: training and inference. The conversation depicts Stark as the AI researcher and Parker as the developer, emphasizing their collaborative roles in harnessing AI to enhance their superhero missions.