Define a stop sequence in an LLM API call

**A stop sequence** is a string you pass into an LLM API call to tell the server: stop generating the moment the output ends with this. You can usually pass several stop sequences at once, and the server stops as soon as any of them matches. Mechanically, the runtime watches the tokens as they are produced. After each token, it checks whether the latest tail of the decoded text matches one of the stop strings. If yes, it halts generation immediately, strips the matching characters from the response, and closes the stream. If no, it continues to the next decode step. This is different from two other ways generation can end. `max_tokens` is an unconditional cap on output length, applied regardless of what was generated. The model's special EOS (end of sequence) token is something the model itself emits when it has finished its thought, learned during training. A stop sequence is content-conditional, defined by the caller, and triggered by what the model actually wrote. Typical use cases: stop at a section heading like `\n## ` so the model does not start a new section, stop at `\n\nUser:` so the model does not roleplay the user's next turn, or stop at a custom delimiter that ends a structured response.

**A stop sequence** is a content-conditional termination string (or list of strings) supplied by the caller. The decoder checks the current output suffix against the stop list at every step; the first match halts generation and the matching tokens are removed from the returned text. The check happens just outside the sampling loop, after each new token is appended to the output buffer. Most engines implement it as a suffix-match against the detokenized string, not against token IDs, because tokenizer boundaries do not line up with arbitrary stop strings (a stop like `</tool>` may straddle two BPE tokens). vLLM, TGI, and the OpenAI runtime all stream tokens out only up to the point where a match becomes possible, sometimes buffering one or two tokens to handle the case where the match completes on the next token. Three termination mechanisms coexist in one request. `max_tokens` is an unconditional integer cap; the runtime halts after that many output tokens regardless of content, and the response carries `finish_reason: length`. The model's learned EOS token is a special vocabulary entry the model emits when it considers the response complete; this produces `finish_reason: stop` with an empty stop string. A user stop sequence is the third option, producing `finish_reason: stop` with the matched string surfaced for debugging. Typical uses: terminate at a role tag (`\nUser:`, `\nHuman:`) to prevent the model from continuing a roleplayed dialogue, terminate at a structural delimiter (`</tool>`, `}` for a JSON object, `---` for a section break), or terminate at a custom marker in few-shot prompts that separate examples. OpenAI accepts up to four stop strings per request; Anthropic accepts a list with no documented cap. The cost is small and the lever is the cleanest way to bound generation by content.