DEV Community: Saranya Madala

What happens when we type a prompt into ChatGPT??

Saranya Madala — Fri, 05 Jun 2026 18:34:47 +0000

After using ChatGPT for months I've realised what ChatGPT is doing to my words or sentences (input/prompt) before it even starts thinking...

For this there are 4 varieties of steps involved in it:

Step 1 : Tokenization (Our text gets chopped into tokens)
ChatGPT or any LLM models dont understand sentencesor words they only understand tokens (later they get converted into numbers we will discuss this in furthur steps )

So every sentence or text we type as a prompt into the model it gets converted into tokens. This is sometimes also called chunking that are roughly syllables or short words .

Lets have a look to the example block of code :

Weird fact: " ChatGPT" (with a leading space) is a different token than "ChatGPT". The model treats them differently. Whitespace is part of the vocabulary.

Step 2 : Vectorization (Each tokens becomes a vector)

As we know a fact where LLM models can only undestand numbers so it converts the tokens into vectors i.e., giving some numbers to each and every chunked token . Basically ,a vector is a list of approx 12,000 numbers — that encodes its meaning. Similar words land near each other in this space. "king" and "queen" are close. "king" and "Apple" are far apart.

This is the moment where language becomes math !!

*Step 3: The Transformer layer *

Our token vectors flow through approx 96 layers of attention. At each layer, every token "looks at" every other token and decides how much to weight it. This is how the model figures out that "it" in "the animal didn't cross the street because it was tired" refers to the animal, not the street.

No rules. No grammar book. Just learnt weights doing matrix multiplication billions of times.

Step 4: Sampling (One token gets sampled)
Basically the model does not output a sentence right away. It uses probability distribution for its entire vocabulary by sampling one token at a time and repeats this process until it reaches the stop condition.

For example, let's say
The capital of France is ------?
Paris – 95% probability
Italy - 80% probability
Berlin – 81% probability
Texas - 5 % probability

So Paris has a 95% probability to complete the above sentence; Paris could be the next likely word to appear.

That's it. No reasoning. No understanding. Just: What token is most likely to come next, given everything before it?

The unsettling part: that simple process, scaled up, produces responses that feel like thinking.

Modern AI Landscape - My Understanding

Saranya Madala — Fri, 05 Jun 2026 00:08:37 +0000

Lets start our discussion from 2010 .

Timeperiod 2010 - 2020 we have predictive AI models such as Recommendation systems , customer segmentation etc ..

From 2020 the when the generative models were introduced to the world then the landscape was completely changed . We have this generative era till 2022 .
Then industry was stepped into a new era called "Augumentation" models like AI Copilot . This was continued from 2022-2024 .

Then came AI Agents—one of the most transformative innovations of the modern AI era. Unlike traditional AI systems that primarily generate responses, agents can reason, plan, use tools, and execute tasks autonomously. Today, the industry is rapidly evolving toward Autonomous Systems, where multiple specialized agents collaborate through orchestration frameworks to solve complex real-world problems.

The best AI Timeline :

Traditional ML
↓
Deep Learning
↓
Transformers (2017)
↓
Foundation Models
↓
LLMs (GPT Era)
↓
Prompt Engineering
↓
Embeddings
↓
Vector Databases
↓
RAG
↓
Function Calling
↓
AI Agents
↓
Agent Frameworks
↓
Multi-Agent Systems
↓
MCP
↓
Agentic AI
↓
Autonomous AI Organizations

Just in the span of 6 years we saw a drastic change in the evolution of AI. Can't imagine how this AI is going to be in the next few years.

ai #machinelearning #python

Documenting my AI Learning Journey !!

Saranya Madala — Thu, 04 Jun 2026 23:39:47 +0000

As a recent Computer Science graduate, I've decided to commit myself to learning AI consistently and sharing that journey publicly.

In a world where many people document their daily lives, I want to document something that is transforming my future—my growth as an AI Engineer.

This is my very first dev post, and the beginning of what I hope will be a long series of learning notes, project breakdowns, insights, challenges, and discoveries in AI, Machine Learning, LLMs, Agents, RAG, System Design, and beyond.

My goal isn't just to consume knowledge but to build, experiment, and share what I learn along the way.

Here's to staying curious, learning every day, and being consistent even when progress feels slow.

Wish me luck on this journey!

AI #ArtificialIntelligence #MachineLearning #LLM #AIEngineering #LearningInPublic #BuildInPublic #SoftwareEngineering #TechJourney #CareerGrowth