Everyone uses AI. Almost no one understands how it actually works. People throw around words like transformers, embeddings, RAG, agents, RLHF… …as if everyone already knows. Most don't. And to be honest? AI isn't that complicated once you understand its mental models. ChatGPT. Claude. Midjourney. Cursor. Coding agents. They all make sense once you understand the 20 ideas below. You don't need a PhD. Zero jargon. Just simple explanations and visual resources. Save this. You'll use it again.
PART 1: HOW AI ACTUALLY WORKS (The foundation everything is built on)
1. Neural Networks
The brain of every AI model.
A neural network is a sequence of layers.
→ Data enters through the input layer → Passes through hidden layers → Comes out as a prediction.
Each connection has a "weight"—a small score that controls how much influence one neuron has over the next.
Training = adjusting billions of these weights until the result is accurate.
A simple idea. Insane at scale.
GPT-4 has ~1.8 trillion parameters. Claude 3 Opus has hundreds of billions.
All from the same basic concept: neurons in layers with adjustable connections.
2. Tokenization
Before AI reads your text, it breaks it into pieces called tokens.
They aren't always full words.
"playing" → "play" + "ing"
"ChatGPT" → "Chat" + "G" + "PT"
"dog" → "dog" (stays whole)
Why not just use full words?
Language is chaotic. New words. Typos. Mixed languages. A fixed vocabulary of words would be impossibly large.
Tokens are reusable building blocks.
Even if the model has never seen a word, it can understand it by breaking it into familiar fragments.
Rule of thumb: 1 token ≈ 0.75 words.
1000 tokens ≈ 750 words.
3. Embeddings
Once text is tokenized, each token is converted into a number.
That number is an embedding, a vector representing meaning.
Think of it as Google Maps for words.
→ "Doctor" and "Nurse" are located close together
→ "Doctor" and "Pizza" are located far apart
→ "King" minus "Man" plus "Woman" ≈ "Queen"
The model doesn't understand words like you do.
It understands distance and direction.
This is what powers:
→ Semantic search
→ Recommendations
→ RAG systems
Everything that "understands intent" uses embeddings under the hood.
4. Attention
The word "Apple" means different things:
→ "I ate an Apple" → fruit
→ "I bought Apple stock" → company
Embeddings alone can't solve this.
Attention can.
Attention allows each word to look at every other word in a sentence and decide what's important.
In "She bought Apple stock":
→ "Apple" pays close attention to "stock" and "bought"
→ The model concludes: company, not fruit
Before attention, models read left to right. Slow. Limited.
After attention, models see the whole sentence at once.
This single idea unlocked modern AI.
5. Transformers
The architecture powering almost every AI model today.
Introduced in 2017 in a research paper called "Attention Is All You Need."
The breakthrough: instead of reading text word by word, it processes everything in parallel using attention.
How it works:
→ Text → Tokens → Embeddings → Stacked attention layers → Result
Each layer refines understanding:
→ Early layers: grammar, basic structure
→ Middle layers: relationships between words
→ Deep layers: complex reasoning
The result: immensely faster training and much better results.
GPT. Claude. Gemini. Llama. Mistral.
They are all transformers.
If you understand this single architecture, you understand modern AI.
PART 2: HOW LLMS WORK (What's actually happening when you chat with an AI)
6. LLM (Large Language Models)
An LLM is a transformer trained on a massive amount of text.
Books. Websites. Code. Wikipedia. Reddit.
Trillions of tokens.
The training task sounds too simple to be powerful:
→ Predict the next token.
That's it.
But when you repeat this across trillions of examples, something extraordinary happens.
The model learns grammar. Then reasoning. Then how to write code, translate languages, solve math problems.
No one ordered it to do any of that.
It emerged from next-token prediction at scale.
"Large" = hundreds of billions of parameters. Training cost = millions of dollars.
ChatGPT, Claude, Gemini → they are all LLMs.
7. Context Window
Every AI model has a memory limit.
It's called the context window.
It's the maximum number of tokens the model can "see" at once: your prompt + its response + conversation history.
Early GPTs: ~4,000 tokens. GPT-4: 128,000 tokens. Claude 3.5: 200,000 tokens. Gemini 1.5 Pro: 1,000,000 tokens.
Larger window = more context = better answers.
But there's a catch.
Models don't read everything equally.
They focus on the beginning and the end of the context.
The middle? Often ignored.
This is called the "Lost in the Middle" problem.
Large context window ≠ perfect memory.
Understanding this explains why AI sometimes "forgets" something you clearly mentioned.
8. Temperature
When AI generates text, it doesn't always choose the single most likely next word every time.
It has a dial called temperature.
→ Temperature = 0: always chooses the safest, most predictable word
→ Temperature = 1: chooses with more creativity, more variety
→ Temperature = 2+: becomes extreme, sometimes incoherent
Low temperature → use for: code, data, summaries
High temperature → use for: brainstorming, creative writing, variations
Most tools set this for you automatically.
But understanding it explains why AI sometimes feels "boring" and sometimes surprises you.
9. Hallucination
AI lies with confidence.
Not on purpose. It literally can't help it.
Here's why.
An LLM isn't looking for truth.
It's predicting what the most likely next token is.
If a false statement looks like something that "should come next" based on training patterns, it generates it.
No fact-checking. No database lookup. Pure pattern matching.
So it will:
→ Cite a research paper that doesn't exist
→ Invent an API function that was never created
→ State a false historical "fact" with total confidence
This is called hallucination.
The fix: never trust AI output on factual data without verifying.
Use RAG (concept 16) to ground it in real data.
10. Prompt Engineering
The way you ask changes everything.
Same model. Same question. Wildly different results based on how you frame it.
Bad prompt: → "Explain APIs" → Gets: vague, surface-level answer
Good prompt: → "Explain how REST APIs handle authentication. Give a real example with code. Assume I'm a junior developer." → Gets: specific, structured, immediately useful
Prompt engineering is just clear communication.
The tricks that actually work: → Give context ("I'm building a SaaS for X") → Assign a role ("Act as a senior backend engineer") → Show examples ("Here's a format I like: ___") → Be specific about output ("Give me 5 options as a numbered list") → Break complex asks into steps
Prompt engineering isn't a hack.
It's the main way you communicate with the model.
PART 3: HOW AI MODELS IMPROVE (How raw models become useful products)
11. Transfer Learning
Training from scratch is expensive.
Incredible amounts of data. Massive compute. Weeks of training.
Transfer learning solves this.
You take a model already trained on a huge general task and adapt it for something specific.
You don't start from zero. You build on a foundation.
Think of it this way:
→ You already know how to ride a bicycle
→ Learning to ride a motorcycle is much faster because of that
→ You transfer what you already know
This is how almost all AI products work today:
→ OpenAI trains a massive foundation model
→ Companies fine-tune it for their specific use case
→ Saves millions in compute and months of training
No company trains from scratch anymore.
12. Fine-Tuning
Transfer learning explains the concept.
Fine-tuning is how you execute it.
You take a pre-trained model and continue training it on a smaller, specific dataset.
The model already masters "language."
Now you are teaching it your particular field.
Examples:
→ Medical model fine-tuned on clinical notes
→ Legal model fine-tuned on contracts
→ Coding model fine-tuned on GitHub
The result: a model that responds perfectly for your use case.
The cost: you need to update billions of parameters.
That requires heavy compute: multiple GPUs and serious infrastructure.
(This is why LoRA, the next concept, matters so much).
13. RLHF (Reinforcement Learning from Human Feedback)
Fine-tuning makes models specialized.
RLHF is what makes them feel helpful and safe.
Without it: the model just predicts text. Fluent, but not aligned.
With it: the model learns what humans actually prefer.
How it works:
→ A prompt is shown to the model → The model generates multiple responses → Humans rank the responses → The model learns to prefer what humans prefer
This repeats thousands of times.
The model builds a sense of a "good answer":
→ Clear
→ Helpful
→ Honest
→ Safe
This is why ChatGPT and Claude feel like assistants, not random text generators.
Without RLHF, they would still be impressive. But much less useful, less reliable, and much harder to control.
14. LoRA (Low-Rank Adaptation)
Fine-tuning is powerful but expensive.
Updating billions of parameters requires multiple GPUs and serious infrastructure.
LoRA solves this.
Instead of changing the whole model, LoRA:
→ Keeps the original model frozen
→ Adds tiny trainable layers on top
→ These layers are a fraction of the full model size
The key: most changes in fine-tuning are small.
You don't need to rewrite the whole model.
You just need specific small adjustments.
Results:
→ Fine-tuning on a single consumer GPU: possible
→ Storing one base model + swapping different LoRA adapters: practical
→ Multiple specialized models without massive storage: achieved
LoRA is why open-source AI exploded.
Suddenly, anyone could fine-tune powerful models on a laptop.
15. Quantization
Models are getting huge.
Running them requires massive memory and compute.
Quantization makes them smaller and cheaper to run.
How: by reducing the precision of each weight.
A weight stored in full precision uses 32 bits.
Quantized to 4 bits → 8x smaller.
The incredible part: the quality loss is often surprisingly small.
This is why you can now:
→ Run LLaMA on a MacBook
→ Run Mistral locally on a consumer GPU
→ Use powerful models on a phone
Without quantization, large models would stay locked in data centers.
With quantization, they run on your machine.
PART 4: HOW REAL AI SYSTEMS ARE BUILT (What's behind the products you actually use)
16. RAG (Retrieval-Augmented Generation)
LLMs hallucinate because they answer from memory.
RAG fixes this by letting them look up information first.
How it works:
The user asks a question
The system searches for relevant documents in a knowledge base
Those documents are given to the model as context
The model answers using real info, not guesses
Think of it this way:
→ Closed-book exam (no RAG): answers from memory, often gets it wrong
→ Open-book exam (with RAG): looks at the source, much more accurate
Why it's powerful:
→ No retraining needed when your data changes, just update the docs
→ Model always works with current, accurate info
→ Drastically reduces hallucination
Every serious AI product uses RAG.
Customer support bots. Legal tools. Medical assistants. Internal knowledge bases.
17. Vector Databases
RAG needs to find the right documents fast.
But how do you search millions of documents by meaning and not just keywords?
Vector databases.
How they work:
Each document is converted into an embedding (a vector of numbers).
These vectors are stored in the database.
When a user asks a question, the question is also converted into a vector.
The database finds the vectors closest to the question vector.
It returns the most semantically similar documents.
Why this is better than keyword search:
→ "heart disease treatment" finds documents about "cardiac care protocols"
→ Even if the exact words don't match, the meaning does.
Tools: Pinecone, Qdrant, Weaviate, pgvector.
Vector databases are what make AI systems "understand" and not just match text strings.
18. AI Agents
An LLM answers prompts.
An AI agent actually does things.
The difference:
→ LLM: you ask, it answers, end
→ Agent: you give it a goal, it plans, takes action, checks results, adjusts, repeats
The agent loop:
Think → Act → Observe → Repeat
Example: a coding agent fixing a bug
→ Reads the issue
→ Explores the source code
→ Identifies the flaw
→ Writes a fix
→ Runs tests
→ Observes what failed
→ Adjusts the fix
→ Repeats until done
The model is the brain. Tools are the hands.
What tools can agents use?
→ Web search
→ Code execution
→ File system
→ APIs
→ Email / calendar
→ Databases
Agents are what turn AI from a simple chatbot into a coworker.
19. Chain of Thought (CoT)
Sometimes AI gives a wrong answer not because it's stupid.
But because it jumped to the answer too fast.
Chain of Thought fixes this.
Instead of asking for the final answer directly:
→ "Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?"
You tell it to think step-by-step:
→ "Solve step-by-step: Speed = 60 mph. Time = 2.5 hours. Distance = Speed × Time = ?"
The model walks through the reasoning:
→ Step 1: Identify the formula
→ Step 2: Plug in the numbers
→ Step 3: Calculate
It's much more reliable for math, logic, and multi-step problems.
The key: giving the model space to think, not just react.
This is why prompts like "think step by step" or "reason through this carefully" actually work.
20. Diffusion Models
So far everything has been about text.
Diffusion models explain how AI generates images.
The process is counter-intuitive.
The model doesn't learn to draw.
It learns to destroy images.
Training:
→ Starts with a real image
→ Adds noise step-by-step until it's pure static
→ Trains the model to reverse this, removing noise step-by-step
Generation:
→ Starts with pure noise
→ Model removes noise step-by-step
→ Guided by your text prompt
→ The image emerges from the randomness
The name comes from physics: particles diffusing randomly through a medium, like ink spreading in water.
Here, the model learns to reverse that diffusion.
It's not just images anymore:
→ Video (Sora, Runway)
→ Audio
→ 3D content
→ Drug molecules
Diffusion models are how AI generates anything visual.
Those are the 20. Let me summarize:
How AI works:
→ 1. Neural Networks: layered pattern learning
→ 2. Tokenization: breaking text into pieces
→ 3. Embeddings: meaning as numbers
→ 4. Attention: context changes meaning
→ 5. Transformers: the architecture behind everything
How LLMs work:
→ 6. LLM: next-token prediction at massive scale
→ 7. Context Window: memory limits and the middle problem
→ 8. Temperature: the creativity dial
→ 9. Hallucination: confident and wrong
→ 10. Prompt Engineering: how you communicate
How models improve:
→ 11. Transfer Learning: building on what exists
→ 12. Fine-Tuning: specializing a model
→ 13. RLHF: teaching it to be helpful
→ 14. LoRA: fine-tuning without the cost
→ 15. Quantization: running big models on small machines
How real systems are built:
→ 16. RAG: search first, then answer
→ 17. Vector Databases: search by meaning
→ 18. AI Agents: from answering to doing
→ 19. Chain of Thought: giving it space to think
→ 20. Diffusion Models: from noise to image
Now you understand how AI actually works.
Most people using AI every day don't know this.
That gap is your advantage.
If this was helpful:
→ Repost to share it with your network
→ Follow @chesny for more breakdowns like this
→ Save this for reference
I write about AI, product building, and systems that work while you sleep.
