1. Large Language Model (LLM)
An AI system that predicts the next word in a sentence.
Learns language patterns from massive amounts of text.
A neural network trained to predict the next word or "token" in a sequence
.
Example:
You type: “All that glitters is”
The model predicts: “not gold”
Like auto-complete on steroids.
2. Tokenization
Breaking text into small pieces (tokens) that the model can understand.
Tokens can be words, parts of words, or symbols.
The process of breaking down input text into smaller, discrete units (tokens) so the model can process natural language effectively
Example:
Sentence: “Running fast”
Tokens: run + ing + fast
Helps AI understand verb tense and meaning.
3. Vectorization
Converting tokens into numbers that represent meaning.
Similar words have similar numbers.
Mapping words into a multi-dimensional space (vectors) where words with similar meanings are clustered together
Example:
“King” and “Queen” are close together
“King” and “Banana” are far apart
Like placing words on a meaning map.
4. Attention
Helps AI understand context by focusing on important nearby words.
This is the key breakthrough that made LLMs powerful.
A mechanism that allows the model to look at nearby words to derive context, helping it distinguish between different meanings of the same word
Example:
“Apple is tasty” → fruit
“Apple revenue increased” → company
Humans do this naturally; attention lets AI do the same.
5. Self-Supervised Learning
AI learns without humans labeling data.
It hides parts of text and tries to guess the missing parts.
A scalable training method where the model learns from the inherent structure of data (like filling in blanks) without needing human labels
Example:
Sentence: “The sky is ___”
AI learns the answer is likely “blue”
Like solving fill-in-the-blanks automatically.
6. Transformer
The engine behind modern AI models.
Uses multiple layers of attention to understand deep meaning.
The specific architectural algorithm that uses attention layers and neural networks to predict the next token
Example:
Understanding sarcasm
Knowing emotions like fear, hunger, or intent
Like reading between the lines in a conversation.
7. Fine-Tuning
Teaching a general AI to behave in a specific way.
Makes models domain-specific.
Taking a base model and training it further on specific data (like medical or financial records) to make it an expert in a particular field
Example:
Medical AI learns medical language
Finance AI learns financial terms
Same brain, different training focus.
8. Few-Shot Prompting
Giving examples inside the prompt to guide the AI.
Example:
Q: Where is my order?
A: Please share your order ID.
Q: Where is my parcel?
AI learns the expected response style instantly.
9. Retrieval-Augmented Generation (RAG)
AI fetches relevant documents before answering.
Reduces hallucinations.
Enhancing an LLM by giving it access to real-time, relevant company documents from a database to provide more accurate answers
Example:
Customer asks about refund
AI pulls refund policy from company docs
Like checking a rulebook before answering.
10. Vector Database
Stores documents as vectors (meaning-based).
Finds relevant info using semantic similarity, not keywords.
Example:
Query: “I’m upset with payment”
AI retrieves docs about refunds or complaints
Even if “upset” isn’t written explicitly.
11. Model Context Protocol (MCP)
Lets AI connect to external systems and tools.
AI can act, not just talk.
A way for models to securely connect with external servers or tools (like booking a flight) to execute tasks
Example:
AI checks airline databases
Books a flight automatically
Like a smart assistant with hands.
12. Context Engineering
Managing everything AI knows before responding:
Past chats
User preferences
Documents
External data
- The practice of managing user preferences and summarizing long chat histories to keep the model's "memory" efficient
Example:
Summarizing old conversations
Remembering your preferences
Like a human assistant with memory.
13. Agents
Long-running AI systems that plan and act autonomously.
Can use tools, APIs, and other agents.
Long-running processes that can query LLMs and external systems independently to complete a user's goal
Example:
Travel agent AI books flights, hotels, and sends emails
Like a personal secretary that never sleeps.
14. Reinforcement Learning (RLHF)
AI improves by learning which answers humans prefer.
Good answers are rewarded; bad ones are penalized.
Training models by having humans rank responses, rewarding "good" paths and penalizing "bad" ones to improve the user experience
Example:
ChatGPT asks: Which answer is better?
Your choice trains the model
Like training a dog using rewards.
15. Chain of Thought
AI reasons step by step instead of guessing.
Improves accuracy for complex problems.
Training a model to break down complex problems step-by-step, which significantly improves the quality of its reasoning
Example:
Solving math step-by-step
Explaining reasoning clearly
Like showing your work in exams.
16. Reasoning Models
Advanced models that focus on logical problem-solving.
Can plan, infer, and analyze deeply.
Example:
Debugging code
Solving puzzles
Making strategic decisions
17. Multimodal Models
AI that works with text, images, audio, and video.
AI that can process and generate more than just text, including images and video
Example:
Counting objects in an image
Generating images or videos from text
Like human senses combined.
18. Small Language Models (SLMs)
Smaller, cheaper, faster models for specific tasks.
Used by companies for privacy and control.
Models with fewer parameters (3M to 300M) than LLMs, making them cheaper and faster for specific company tasks
Example:
Customer support chatbot
Sales assistant
Not smart at everything, but great at one thing.
19. Distillation
Teaching a small model using a large model as a teacher.
Keeps performance but reduces cost.
The process of creating a smaller "student" model that mimics a larger "teacher" model to reduce costs while maintaining performance
Example:
Senior employee trains a junior
Junior does the job faster and cheaper.
20. Quantization
Reducing number precision to save memory and cost.
Used during deployment, not training.
Reducing the memory size of a model's internal weights (e.g., from 32-bit to 8-bit) to make it cheaper to run in production
Example:
Compressing a video without noticeable quality loss
AI runs faster and cheaper.
Below is a visual, diagram-based explanation of how an LLM is trained.
Overall Training Pipeline (Big Picture)
Raw Text
↓
Tokenization
↓
Vectors (Numbers)
↓
Transformer (Attention + Neural Networks)
↓
Next-Token Prediction
↓
Loss Calculation
↓
Backpropagation (Learning)
↓
Trained LLM
Think of this as a factory line that turns text into intelligence.
Tokenization Diagram (Breaking Text)
Input sentence:
"AI is learning fast"
Tokenization:
AI | is | learn | ing | fast
Diagram:
Sentence
↓
[ AI ] [ is ] [ learn ] [ ing ] [ fast ]
Tokens are the smallest pieces of meaning the model works with.
Vectorization Diagram (Meaning → Numbers)
Each token becomes a vector (numbers):
AI → [0.21, -1.34, 0.88, ...]
learn → [0.25, -1.30, 0.91, ...]
fast → [0.90, 0.10, 0.77, ...]
Conceptual Space:
Meaning Space (Vectors)
cat ●────● dog
\
\
● AI
\
● learning
Similar meanings are close together.
Transformer + Attention Diagram (Core Brain)
Without Attention (old models)
Word → Word → Word → Word
With Attention (Transformers)
┌──────────┐
Token 1 ───► Attention│
Token 2 ───► Attention│───► Contextual Meaning
Token 3 ───► Attention│
└──────────┘
Attention Example:
"I ate an apple"
apple ──► fruit
"Apple released a phone"
apple ──► company
Attention decides what matters.
Next-Token Prediction Diagram
Training objective:
Input: "The sky is"
Target: "blue"
Prediction flow:
[The] [sky] [is]
↓
Transformer
↓
Predicted next token → "blue"
LLMs learn by guessing the next word.
Loss Calculation Diagram (Error Measurement)
Predicted: "green"
Actual: "blue"
Difference = Loss
Diagram:
Prediction ────┐
├──► Loss Function ───► Error Value
Actual ────┘
Bigger mistake = bigger loss.
Backpropagation Diagram (Learning Happens Here)
High Loss
↓
Adjust Weights
↓
Lower Loss Next Time
Flow:
Output Error
↑
Neural Network Weights Updated
↑
Transformer Layers
Like correcting mistakes after an exam.
Self-Supervised Learning Diagram
No humans label data.
Original text:
"All that glitters is gold"
Training samples:
Input: "All that glitters is"
Target: "gold"
Diagram:
Existing Text
↓
Mask / Shift
↓
Predict Missing Token
Text teaches itself.
Full Training Loop Diagram
Text Batch
↓
Tokenize
↓
Vectors
↓
Transformer
↓
Prediction
↓
Loss
↓
Backpropagation
↓
Repeat (Trillions of Times)
This loop runs on GPUs/TPUs for weeks or months.
Fine-Tuning & RLHF Diagram (Behavior Training)
Fine-Tuning
Question → Model → Answer
↓
Is this correct?
↓
Update Weights
RLHF
Answer A Answer B
↓ ↓
Human picks better one
↓
Reward / Penalty
This makes the model helpful and safe.
Final Mental Model (One Diagram)
Text
↓
Tokens
↓
Vectors
↓
Attention (Context)
↓
Transformer Layers
↓
Next Word Prediction
↓
Human Feedback
↓
Aligned LLM
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