Flashcard: what is a stop sequence in an LLM API call and what is it used for?

**A stop sequence** is a string you pass to the LLM API as a hint: the moment the model generates that string during its reply, generation halts. The stop string itself is stripped from the returned output, so you get a clean response that ends right before it. Most APIs let you pass a list of stop sequences, not just one. Claude, OpenAI, and Google all support multiple stop strings, and the model halts on whichever one appears first. The most common uses are structural. If you are using a few-shot template where each example ends with '\n\nHuman:', you set the stop sequence to '\n\nHuman:' so the model writes one assistant turn and then halts instead of inventing the next user message. If your prompt asks for JSON wrapped in <answer> tags, you can stop on '</answer>' to guarantee a clean cut. The key thing to understand is that a stop sequence is a content boundary, not a length cap. It says terminate when this content appears. The max-tokens parameter says terminate after this many tokens regardless of content. They are different tools for different jobs and you usually set both.

**A stop sequence** is a content-based termination signal: a string (or list of strings) the model halts on the moment generation produces it, with the stop string itself excluded from the returned output. It is the cleanest way to enforce a structural boundary in model output. Three typical uses cover most production prompts. Boundary enforcement in few-shot templates: when your prompt has the pattern Human: ... Assistant: ... Human: ... Assistant: ..., setting the stop sequence to '\n\nHuman:' guarantees the model writes one assistant turn and stops, rather than inventing the next user message. Format termination on tag-wrapped output: stopping on '</answer>' or '```' or '---' gives a deterministic end to a structured section. Agent-loop sentinels: a ReAct or tool-using agent stops on 'Final Answer:' or '</tool>' to mark the end of one reasoning-action cycle. The critical distinction from **max-tokens** is the trigger condition. Max-tokens fires when the model has produced N output tokens regardless of what those tokens say (a length ceiling, blunt). Stop sequence fires when the running output matches the stop string (a structural boundary, precise). They are not alternatives; production prompts set both, with stop sequences expressing intent and max-tokens as the safety net in case the model never reaches a stop. One implementation caveat: stop sequences match on token boundaries in some APIs and on character boundaries in others. For multi-token strings, test the exact tokenization to confirm the match works as expected.