CORE GUIDE
Next-token generation
Language models produce sequences one token decision at a time; fluent long-form behavior emerges from repeated local predictions conditioned on the growing context.
Mental model
Generation is an autoregressive loop: the model scores possible next tokens from the current context, a decoding policy selects one, that token is appended, and the process repeats until a stop condition.
Why it matters
Many product failures are easier to reason about once generation is treated as a repeated stochastic process rather than a one-shot retrieval of a hidden answer. Early token choices change the context seen by later steps, so small probability differences can compound into different explanations, code paths or tool arguments.
01
Reason about the loop, not a single completion
At each step the model maps the current token sequence to a probability distribution over the vocabulary. Decoding chooses a token, the application appends it to the sequence, and the model is called again with the updated context. Stop tokens, length limits and runtime policies end the loop. Because each selected token becomes evidence for the next prediction, generation path dependence matters.
02
Example: two plausible openings diverge
A support draft begins with either 'Yes' or 'It depends'. Both tokens were plausible under the prompt, but each changes the subsequent conditional distribution. The first path tends toward a confident policy statement while the second invites qualification. The product should therefore evaluate complete behavior across repeated runs instead of treating one fluent sample as the model's fixed answer.
Common failure modes
- Treating a model response as if it were fetched atomically from a database.
- Debugging only the final sentence while ignoring earlier token choices that shaped it.
- Assuming deterministic-looking output proves the underlying distribution is certain.
Engineering heuristics
- Inspect multiple samples when behavior risk depends on generation variance.
- Separate model token probabilities from runtime stop, retry and validation policies.
- Evaluate complete trajectories for tasks where early choices constrain later steps.
Takeaways
- 01Generation is a repeated conditional decision process.
- 02Each chosen token becomes context for the next prediction.
- 03Stable product behavior requires runtime contracts around probabilistic generation.
Reading evidence
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Used in
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