An autoregressive model is a type of neural network that generates output one token at a time, where each new token is predicted based on all previously generated tokens. This sequential, left-to-right generation process is the fundamental mechanism behind how modern large language models produce text.
Autoregressive models work by learning the probability distribution of the next token given all the tokens that came before it. During training, the model sees massive amounts of text and learns statistical patterns about which tokens are likely to follow which sequences. At inference time, it uses these learned patterns to generate text one piece at a time.
The process is analogous to how a person might complete a sentence: given the beginning 'The weather today is,' you would predict the next word based on your knowledge of language and context. An autoregressive model does the same thing, but with mathematical precision across a vocabulary of tens of thousands of tokens, drawing on patterns learned from billions of examples.
Each generation step involves running the full model forward pass to produce a probability distribution over the entire vocabulary. A sampling strategy such as greedy decoding, top-k sampling, or temperature-based sampling then selects which token to actually output. The chosen token is appended to the sequence, and the process repeats until the model produces a stop token or reaches the maximum length.
This sequential nature has important implications for performance and cost. Because each token depends on all previous tokens, generation cannot be easily parallelized. The time to generate a response scales linearly with the number of output tokens, and each step requires accessing the model's full attention history. Techniques like KV-caching, speculative decoding, and batching have been developed to mitigate these costs.
The model tokenizes the input text and processes all tokens in parallel through its transformer layers. This prefill phase generates internal representations and caches key-value pairs for efficient subsequent generation.
Using the representations from the input and any previously generated tokens, the model computes a probability distribution over its entire vocabulary, indicating how likely each possible next token is.
A sampling strategy selects the actual next token from the probability distribution. Temperature controls randomness, top-k limits the candidate pool, and top-p (nucleus sampling) dynamically adjusts the selection threshold.
The selected token is appended to the sequence, the cache is updated, and the model predicts the next token. This loop continues until a stop condition is met: an end-of-sequence token, a maximum length, or a stop sequence specified by the user.
When a developer types the beginning of a function, an autoregressive model predicts the most likely subsequent tokens one at a time, completing the function body, parameter types, and documentation based on the patterns it learned from training on code.
When you send a message to ChatGPT or Claude, the model generates its reply token by token. This is why you can see the text appearing word by word in streaming mode, as each token is produced sequentially by the autoregressive process.
A user provides a story premise, and the model extends it by generating one token at a time. Each word choice influences all subsequent words, which is why the temperature setting can dramatically affect whether the story is predictable or creative.
The autoregressive approach is what makes modern LLMs possible. Understanding this mechanism explains key behaviors users observe: why models sometimes hallucinate (they commit to early tokens that lead down incorrect paths), why streaming works (tokens are produced sequentially), and why longer outputs cost more (each token requires a model forward pass). It also explains the fundamental performance characteristics that engineers must optimize around.
Respan lets you monitor the key metrics of autoregressive generation in production: tokens per second, time to first token, total generation latency, and output token counts. Understand how generation performance varies across different prompts, models, and load conditions to optimize cost and user experience.
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