The Complete Process of Training AI, LLMs & Intelligence (2026)

Artificial Intelligence is everywhere today — writing content, generating images, answering questions, and even coding. But most people still don’t truly understand how AI models work, how AI learns from data, or what really happens during the process of training AI models.

Visual representation of AI learning process

This article explains, in simple language, how AI works step by step, how machine learning models are trained, how LLMs work, and how generative AI systems like ChatGPT function behind the scenes.

What Is an AI Model?

An AI model is a computer system trained on large amounts of data to recognize patterns and make predictions.

In simple words, an AI model:

Observes data
Learns patterns
Predicts outcomes based on probability

This basic idea explains how AI models are created and why AI does not “think” like humans.

Source:
https://www.ibm.com/topics/artificial-intelligence

How AI Models Work

At a high level, how AI models work can be broken into three stages:

Training – learning from data
Validation – checking accuracy
Inference – producing outputs

AI does not understand meaning or intent.
AI predicts the most likely result based on patterns learned during training.

Source:
https://developers.google.com/machine-learning/crash-course/ml-intro

The Process of Training AI Models (Step by Step)

The process of training an AI model follows a structured pipeline. These are the main stages involved in the process of training AI models.

Step 1: Data Collection

AI models learn from massive datasets such as:

Text (articles, books, websites)
Images
Videos
Audio
Code

The quality and diversity of data directly impact model accuracy and bias.

Source:
https://www.ibm.com/topics/machine-learning-data

Step 2: Data Cleaning and Preparation

Raw data is noisy and unusable in its original form. Engineers clean data by:

Removing duplicates
Fixing errors
Normalizing formats
Labeling data (if required)

This step often consumes the majority of the training timeline.

Source:
https://developers.google.com/machine-learning/data-prep

Step 3: Model Architecture and Modelling

Choosing the structure of the model is called the process of modelling.

Common architectures include:

Neural networks
Convolutional neural networks
Transformer models (used in LLMs)

This decision determines how information flows inside the AI model.

Source:
https://arxiv.org/abs/1706.03762

Step 4: Training the AI Model

This is the core training process of AI.

The model:

Takes input data
Makes predictions
Compares results with correct answers
Adjusts internal parameters

This loop runs millions or billions of times using optimization algorithms.

Source:
https://developers.google.com/machine-learning/crash-course/backpropagation

Step 5: Evaluation and Validation

After training, the model is tested on unseen data to measure:

Accuracy
Error rate
Generalization ability

This ensures the model has learned patterns, not memorized data.

Source:
https://developers.google.com/machine-learning/crash-course/validation

Step 6: Fine-Tuning

Fine-tuning improves performance by:

Training on specialized datasets
Reducing incorrect outputs
Improving alignment and safety

This step is critical for generative AI and LLM systems.

Source:
https://openai.com/research

Step 7: Deployment and Inference

Once training is complete, the model is deployed.

At this stage:

Learning stops
The model only predicts outputs

This is how AI tools and apps are used by real users.

Source:
https://cloud.google.com/ai/docs/inference

How Machine Learning Models Work

Machine learning is a subset of AI.
The machine learning training process follows these steps:

Input data
Feature extraction
Model training
Prediction

These are the fundamental steps of a machine learning model.

Source:
https://www.ibm.com/topics/machine-learning

How AI Learns From Data

AI learns by identifying statistical relationships in data.

For example, when AI sees:
“The capital of France is ___”

It predicts “Paris” because that pattern appears frequently in training data.

This explains how AI learns from data and why biased data creates biased AI.

Source:
https://www.nature.com/articles/d41586-021-01833-0

How LLMs Work (Large Language Models)

LLMs are AI models trained on massive text datasets to predict the next token.

They work by:

Tokenizing text
Converting tokens into numerical vectors
Using transformer layers to analyze context

This explains how LLM works at a fundamental level.

Source:
https://openai.com/research/language-models

Process of Training an LLM

The process of training an LLM includes:

Tokenization
Embedding generation
Transformer-based processing
Error correction using backpropagation

This is the primary training process of generative AI models.

Source:
https://arxiv.org/abs/2005.14165

How ChatGPT Works

ChatGPT is a large language model fine-tuned using human feedback.

When a user enters a prompt:

The input is tokenized
Context is analyzed
The model predicts the next best tokens
Responses are generated word by word

This explains how ChatGPT works in practice.

Source:
https://openai.com/blog/chatgpt

Role of GPUs in AI Training

Training AI models requires massive parallel computation.

GPUs are used because they:

Perform thousands of operations simultaneously
Handle large datasets efficiently
Reduce training time drastically

Modern AI is not possible without GPUs.

Source:
https://www.nvidia.com/en-us/deep-learning-ai/

Common Misunderstandings About AI

AI does not think like humans
AI does not understand meaning
AI does not know truth
AI predicts probabilities

Understanding this removes most AI myths.

Pro Tips for Understanding AI Better

Focus on data quality, not hype
Learn the difference between training and inference
Understand probability-based prediction
Ignore marketing buzzwords

Summary Table: AI Model Training Stages

Stage	Description
Data	AI observes examples
Training	Learns patterns
Validation	Tests accuracy
Fine-tuning	Improves performance
Inference	Produces outputs

Frequently Asked Questions

What is the process of training AI called?

It is called machine learning training, involving optimization and parameter tuning.

How long does it take to train an AI model?

From hours to months, depending on model size and data volume.

Can beginners train AI models?

Yes, beginners can train small models using open-source tools and cloud platforms.

Conclusion

Now you understand:

How AI models work
The process of training AI models
How machine learning works
How LLMs and generative AI systems function

AI is not magic.
AI is data, mathematics, and computation working together at scale.

Author

kashyap aditya
AI & Technology Researcher