How Gemini, GPT-5, and Modern LLMs Actually Work — A Simple Explanation

Artificial Intelligence has changed more in the last five years than in the previous fifty. At the centre of this revolution are Large Language Models (LLMs) — systems like ChatGPT (GPT-5), Google Gemini, Anthropic Claude, and Meta’s LLaMA. They write code, create stories, summarize research, and even reason logically.

But what exactly is happening inside these models?
How do they “understand” language?
Why do transformers matter so much?

This article explains everything — in simple language, without skipping important concepts.

1. What Are Large Language Models (LLMs)?

An LLM is a neural network trained on massive amounts of text to do one core task:

Predict the next word.

That’s it.

But by learning to predict the next word, the model also learns:

Grammar
Facts
Reasoning patterns
Writing style
Programming languages
Problem-solving
Human conversation structure

This “next word prediction” becomes intelligence when scaled to:

Huge datasets

Huge models (billions/trillions of parameters)

Huge compute power

1. Why Transformers Changed Everything

Before 2017, models processed text sequentially — slow, weak, and unable to remember long sequences.

Then came the breakthrough:

“Attention is All You Need” — the Transformer architecture.

Transformers introduced a simple yet powerful idea:

Self-Attention → Let every word look at every other word.

Unlike RNNs/LSTMs, which read text left-to-right, transformers allow parallelism and global understanding.

For example, in the sentence:

“The cat chased the mouse because it was hungry.”

Self-attention helps the model figure out whether “it” refers to cat or mouse by comparing all words at once.

This is the core engine behind LLMs.

1. How Self-Attention Works (Simple Version)

For each word, the model computes:

Query (Q) → What am I looking for?

Key (K) → What information do I contain?

Value (V) → What should I pass on if selected?

Self-attention computes similarity between Q and K:

Attention Score = Similarity(Query, Key)

This score tells the model how strongly one word should pay attention to another.

High similarity = more attention.
Low similarity = ignored.

Finally, attention scores are used to weight the Values (V).

This allows the model to understand:

Context

Relationships

Meaning

Dependencies

This is how models perform reasoning.

1. Positional Encoding — How Models Know Word Order

Transformers don’t read words in order.
So we add positional embeddings (like coordinates) to each word token.

Example:

Token Position Meaning
“Machine” 1 First word
“Learning” 2 Second word

These encodings allow the transformer to learn grammar and structure.

1. How Models Like GPT and Gemini Are Trained

LLMs go through 3 major phases:

Phase 1 — Pretraining

This is where the model learns general language from massive datasets:

Books

Code

Wikipedia

Research papers

Web pages

Public datasets

Goal:
Predict the next word across trillions of sentences.

This teaches the model:

Grammar

Facts

World knowledge

Reasoning structure

Logic patterns

Phase 2 — Supervised Fine-Tuning (SFT)

Humans provide example prompts and ideal responses.

E.g.,

Prompt:
“What are the benefits of using Redis?”

Ideal Answer:

Fast

In-memory

Great for caching

Supports pub/sub

The model learns how to follow instructions.

Phase 3 — Reinforcement Learning with Human Feedback (RLHF)

Humans rank pairs of answers:

Better

Worse

The model is trained to produce better answers.

This is how ChatGPT became conversational.

1. GPT-5 vs Gemini — Are They Different?

Both are transformers, but differ in design philosophy.

GPT-5 (OpenAI)

Focused on:

Long context reasoning

Better memory

Strong coding ability

Natural conversation

Safety and alignment

GPT-5 uses dense transformer blocks but optimized architecture.

Gemini (Google)

Google’s approach focuses on:

Native multimodality

Gemini can process:

Text

Images

Videos

Audio

Code
All inside a single model.

Parallel processing

Gemini models use techniques like Mixture of Experts (MoE) to scale efficiently.

Better integration with Google ecosystem

Search + YouTube + Google Lens + Docs integration.

1. Are LLMs Just Pattern Matchers?

This is a common misconception.

LLMs do learn patterns, but at scale, patterns become:

Reasoning

Planning

Abstraction

Multistep logic

Representation learning

Generalization

For example, prompting:

“If today is Sunday, what day comes after 200 days?”

The model performs implicit mathematical reasoning learned through pattern exposure.

Not perfect, but far beyond simple matching.

1. How Do LLMs “Understand”?

They don’t understand like humans.
They build high-dimensional vector spaces.

Each concept is represented as a point in space:

“Apple”

“Fruit”

“Red”

“Sweet”

The model learns relationships like:

Apple close to fruit

Dog close to animal

Cat adjacent to pet

This is semantic understanding.

1. Why Scaling Laws Matter

A key discovery:
Models get smarter as they get bigger + train on more data + use more compute.

Scaling laws show predictable improvement.

This is why:

GPT-5 > GPT-4

Gemini 1.5 > earlier versions

LLaMA 3 > LLaMA 2

Bigger models → richer representations.

1. How Modern LLMs Reason

LLMs use internal mechanisms for:

Chain-of-thought reasoning

Multi-step planning

Tool usage

Search integration

Memory mechanisms

E.g., GPT-5 and Gemini can:

Call tools

Access web

Run code

Use retrieval (RAG)

Maintain long contexts (1M+ tokens)

This feels like reasoning because the model breaks tasks into steps.

1. The Role of Retrieval (RAG)

Instead of relying only on what the model remembers, RAG allows the model to fetch external knowledge.

Example:

Query: “Explain India’s 2023 inflation rate.”

RAG fetches a relevant data snippet.

The model summarizes using fresh information.

RAG = memory + accuracy + reasoning.

1. Why Prompting Matters

Even the best model fails with bad prompts.

Reason:

Prompts define context

Prompts guide attention

Prompts restrict or expand reasoning path

Good prompting = better results.

1. Are LLMs Safe? (A Brief Note)

LLMs may:

Hallucinate

Generate unsafe content

Mislead

Misinterpret questions

Safety layers include:

Fine-tuning

Ethical filtering

Guardrails

Red-teaming

Models like GPT-5 and Gemini have heavily improved alignment.

1. What the Future Looks Like

We’re moving toward:

Multimodal LLMs

Text + image + video + audio + code.

Agents

Models that plan, act, use tools and APIs.

Personal AI Assistants

Context-aware models that know your work style.

Scientific reasoning models

Used in biology, chemistry, physics.

Efficient, small models

Running on phones and edge devices.

Conclusion

LLMs like GPT-5 and Gemini aren’t magic — they are built on:

Transformers

Self-attention

Large-scale training

Human feedback

Retrieval systems

Massive compute

Their ability to reason emerges from scale, structured training, and deep neural representations.

We are still in the early stages of the AI revolution — and understanding how these systems work is the first step to building with them.

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