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Next Token Prediction
Next token prediction is the fundamental mechanism behind all LLMs. Understanding it explains why LLMs are so powerful, and also why they sometimes produce plausible-sounding but incorrect responses.
10 min•By Priygop Team•Updated 2026
How Next Token Prediction Works
How Next Token Prediction Works
# Simplified illustration of next token prediction
def predict_next_token(context, vocabulary_with_probabilities):
"""
In reality, an LLM computes probabilities for its entire vocabulary
(50,000+ tokens) at each step. This illustration simplifies to 5 options.
"""
print(f"Context: '{context}'")
print()
print("Model's predicted probability for next token:")
# Sort by probability (highest first)
sorted_predictions = sorted(
vocabulary_with_probabilities.items(),
key=lambda x: x[1],
reverse=True
)
for token, probability in sorted_predictions:
bar = "=" * int(probability * 20)
print(f" '{token}': {probability:.0%} |{bar}|")
# The highest probability token is typically chosen
best_token = sorted_predictions[0][0]
print()
print(f"Selected token: '{best_token}'")
return best_token
# Example: predicting what comes after "The sky is usually"
context = "The sky is usually"
predictions = {
"blue": 0.45,
"clear": 0.25,
"gray": 0.15,
"dark": 0.10,
"bright": 0.05,
}
next_token = predict_next_token(context, predictions)
print(f"New context: '{context} {next_token}'")Diagram
Loading diagram…
Deep Learning ⊂ Machine Learning ⊂ Artificial Intelligence
Why This Matters for Users
- LLMs generate the most statistically likely text, not necessarily the most accurate text
- The same prompt can produce different outputs on different runs because selection is probabilistic
- This is why LLMs can be confidently wrong: plausible-sounding text and accurate text are not the same thing
- Understanding this helps you know when to trust AI output and when to verify it
Key Takeaways
- Next token prediction is the fundamental mechanism behind all LLMs.
- LLMs generate the most statistically likely text, not necessarily the most accurate text
- The same prompt can produce different outputs on different runs because selection is probabilistic
- This is why LLMs can be confidently wrong: plausible-sounding text and accurate text are not the same thing