'Training data,' 'fine-tuning,' 'RLHF' — the words sound mysterious. The actual process is three clear stages.
27 min · Reviewed 2026
The big idea
Modern AI models go through 3 stages: (1) Pretraining — read trillions of words of internet text; (2) Fine-tuning — read carefully curated examples of 'good' responses; (3) RLHF (Reinforcement Learning from Human Feedback) — humans rate pairs of responses and the model learns which kind people prefer. Each stage costs more than the last per data point but uses way less data.
Some examples
GPT-4 pretraining used roughly 13 trillion tokens — more than every book ever published, plus most of the internet.
Fine-tuning uses maybe 100,000 hand-written 'this is how to respond well' examples.
RLHF uses ~1 million human comparisons of 'response A is better than response B.'
Constitutional AI (Anthropic's approach) replaces some human ratings with the model rating itself against a written 'constitution' of values.
Try it!
Read Anthropic's Constitutional AI paper summary on their website (no math, just plain English about how they trained Claude). It takes 15 minutes and you'll understand more about modern AI than 95% of people.
End-of-lesson check
8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-builders-foundations-ai-how-models-are-trained-r9a10-teen
What is the main idea of "How an AI Model Actually Gets 'Trained' (No Math)"?
'Training data,' 'fine-tuning,' 'RLHF' — the words sound mysterious. The actual process is three clear stages.
Use AI as the final authority for the whole decision
Avoid checking the answer once it sounds polished
Focus only on speed instead of judgment
Which concept is most central to "How an AI Model Actually Gets 'Trained' (No Math)"?
pretraining
training data
fine-tuning
RLHF
Which use of AI fits this topic best?
Let the AI decide what matters without your review
Use the answer before checking whether it fits the situation
GPT-4 pretraining used roughly 13 trillion tokens — more than every book ever published, plus most of the internet.
Use the first answer without checking it
What should a careful learner remember about "The rule"?
Use "The rule" as a reminder to verify the AI output before anyone relies on it.
Skip the context so the tool can guess faster
Treat the output as private even after sharing it online
Use the answer without checking the source
You want to use AI after this lesson. What is the safest next step?
Act immediately because the AI answer is written clearly
Use the AI answer as a draft, then check it against a reliable source.
Hide uncertainty so the final answer looks cleaner
Use private or sensitive details before checking permission
How should AI output about training data be treated?
As proof that no other source is needed
As a replacement for context, consent, or expert review
As a draft or helper output that still needs human judgment and verification
As something that becomes correct when it sounds confident
Name one way to verify an AI answer about training data.
Which action would help you apply "How an AI Model Actually Gets 'Trained' (No Math)" responsibly?
Use the tool to avoid thinking through the tradeoff
Keep going even if the output conflicts with a trusted source
Use the first answer without checking it
Fine-tuning uses maybe 100,000 hand-written 'this is how to respond well' examples.
