Autoregressive Models: The “One Word at a Time” Reality
Definition: A model inferring predictions based on its own previous predictions, as seen in transformer-based language models.
How GPT Actually Writes Code
Autoregressive means “predicting the next thing based on the past things.”
- GPT reads:
def hello_world(): - It predicts:
print - It reads
print, then predicts:( - It reads
(, then predicts:'Hello'…
Why This Matters for Debugging
Because the model writes linearly (left to right), it cannot “go back” and fix a mistake it made 3 lines ago in the same generation.
- The “Painted into a Corner” Problem: Sometimes the AI starts a sentence or a code block in a way that makes it impossible to finish correctly. It just keeps hallucinating to try to save the sentence.
Vibe Coding Strategy
If the AI is generating garbage:
- Stop Generation: Don’t let it finish. Save your tokens.
- Rewind: Delete the bad part of the response.
- Steer: Add a comment or a prompt to guide it down a different path.
- Example: The AI starts writing a Class. You wanted a Function. Stop it. Type “Use a functional approach.” Let it continue.
Chain of Thought (CoT)
CoT works because it forces the model to “generate the reasoning” before the answer. Since it’s autoregressive, the “reasoning tokens” become part of the history that informs the “answer tokens.”
- Tip: Always ask complex questions with “Think step by step.” It gives the autoregressive model time to “think” (by generating text) before it has to commit to code.