LLM Core Concepts Explained

What is an LLM?

A Large Language Model (LLM) is an AI trained on vast amounts of text to predict the next token in a sequence. Models like GPT-4, Claude, Gemini, and LLaMA are LLMs.

Key Concepts

Tokens

• Text is broken into tokens (words, sub words, or characters)
• 1 token ≈ 0.75 words in English
• "Hello world" = 2 tokens
• Token limits define how much text a model can process at once

"The quick brown fox" → ["The", " quick", " brown", " fox"] = 4 tokens

Context Window

The context window is the maximum number of tokens a model can "see" at once including your prompt AND the response.

Transformer Architecture

LLMs are built on the Transformer architecture (introduced in 2017).

Key Components

• Embeddings Convert tokens into numerical vectors
• Attention Mechanism Lets the model focus on relevant parts of input
• Feed Forward Layers Process and transform information
• Self Attention Each token "attends" to all other tokens

Self Attention Formula

Attention(Q, K, V) = softmax(QK^T / sqrt(d_k)) x V

Q = Query matrix
K = Key matrix
V = Value matrix
d_k = dimension of key vectors

Key Parameters

Temperature

Controls randomness of outputs.

Top P (Nucleus Sampling)

Limits token selection to top % of probability mass.

• top_p = 0.9 means consider tokens that make up 90% of probability

Max Tokens

Maximum number of tokens in the response.

Embeddings

Embeddings are numerical representations of text in high dimensional vector space. Similar concepts are close together.

# Example: using OpenAI embeddings
from openai import OpenAI
client = OpenAI()

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="Hello world"
)
vector = response.data[0].embedding  # 1536-dimensional vector

Training Stages

1. Pre training

• Train on massive text corpus (internet, books, code)
• Learn language patterns, facts, reasoning
• Very expensive: millions of dollars

2. Fine tuning

• Train on specific domain data
• Adjust model for particular tasks
• Much cheaper than pre training

3. RLHF (Reinforcement Learning from Human Feedback)

• Human raters score model outputs
• Model learns to prefer human rated good answers
• Makes models more helpful, honest, harmless

Hallucination

When an LLM confidently states false information, it's called hallucination.

Causes:

• Training data had errors
• Model doesn't "know" what it doesn't know
• Extrapolates beyond actual knowledge

Mitigation:

• Use RAG (Retrieval Augmented Generation)
• Ask model to cite sources
• Use lower temperature for factual tasks
• Verify outputs with tools/search

RAG (Retrieval Augmented Generation)

User Query
    ↓
[Vector Database Search]
    ↓
Relevant Documents Retrieved
    ↓
LLM receives: Query + Retrieved Context
    ↓
Accurate, grounded response

Popular LLM APIs