How Generative AI Creates Content
Generative AI creates content by learning the statistical patterns in massive amounts of training data, then sampling from those patterns to produce new outputs.
Learning from Data to Generate New Data
Think about how you would describe a cat to someone who has never seen one. You would use patterns you have seen: 'four legs, fur, whiskers, pointed ears, meows'.
Generative AI learns an enormous number of such patterns from its training data. When asked to create something, it samples from these learned patterns to produce new content that follows similar patterns but is not a copy of anything it saw.
For text: it learns what words and phrases tend to follow other words and phrases, across millions of texts.
For images: it learns what pixel patterns (textures, shapes, colors) tend to appear together in images of various subjects.
Deep Learning ⊂ Machine Learning ⊂ Artificial Intelligence
Text Generation: The Core Idea
# Simplified text generation to illustrate the concept
# Real models compute probabilities from billions of learned patterns
import random
# A simplified language model: given context, predict the next word
# (Real LLMs use massive Transformer networks)
# Very simplified bigram model: stores what words tend to follow each word
def build_bigram_model(text):
words = text.lower().split()
model = {}
for i in range(len(words) - 1):
current = words[i]
next_word = words[i + 1]
if current not in model:
model[current] = {}
model[current][next_word] = model[current].get(next_word, 0) + 1
return model
def generate_text(model, start_word, num_words=10):
"""Generate text by sampling from the bigram model."""
current = start_word.lower()
generated = [current]
for _ in range(num_words - 1):
if current not in model:
break
options = model[current]
total = sum(options.values())
words_list = list(options.keys())
weights = [options[w] / total for w in words_list]
next_word = random.choices(words_list, weights=weights)[0]
generated.append(next_word)
current = next_word
return " ".join(generated)
# Train on sample text
training_text = """
The cat sat on the mat. The cat is a small animal.
The dog sat on the floor. The dog is a good animal.
Animals are great companions. Cats and dogs are popular animals.
The cat plays with a ball. The dog plays in the garden.
"""
model = build_bigram_model(training_text)
print("Simple Language Model - Text Generation:")
print()
random.seed(42)
for start in ["the", "animals", "cats"]:
generated = generate_text(model, start, 8)
print(f" Starting with '{start}': {generated}")