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Make context and usage decisions with an explicit accounting model.
This tag brings together inference and token-budget guides for teams deciding what to send, what to reserve for a response and what to measure afterward. The articles avoid fixed assumptions about how many words fit in a token. The relevant unit depends on the model and its tokenizer.
Begin by distinguishing an estimate made before a request from usage returned afterward. Keep the request configuration and relevant provider semantics with both. That record makes it possible to compare prompt changes without silently changing the task. Budget decisions should also preserve enough evidence and output capacity for a useful answer.
2 articles tagged Token budgets
Make LLM consumption predictable with a budget for each task. Separate context limits from output reserves, choose batching and cache policies, and connect estimates, retries, and reported usage to useful completion.
7 min readFollow the transformations between a prompt and a useful response. Understand token IDs, attention masks, model-output shapes, decoding choices, and the metadata that makes an LLM integration easier to debug and evaluate.
7 min read
TensorAPI.com
