Definition
Stop sequence is a parameter setting in Large Language Models that guides when the model should stop generating text during a completion or response. It describes a specific string or sets of strings, which when stumbled upon in the output, tells the model to terminate the generation process. Examples of stop sequences include period (“.”), END, STOP, etc. [1]
Imagine a
scenario where a student seeking admission in university to study medicine wants
to have an idea of where he stands a chance using the help of an AI chatbot, but
at the same time does not want unnecessary extra information. This can be
achieved using Anthropic Claude API;
python
import anthropic
client =
anthropic.Anthropic(api_key='your-api-key')
response =
client.messages.create(
model="claude-3-sonnet-20240229",
max_tokens=200,
stop_sequences=["[STOP]"], # Stop sequence parameter
messages=[
{"role": "user",
"content": "List the top universities in Nigeria and their
cut-off marks for Medicine"}
]
)
print(response.content[0].text)
The stop sequence ensures the AI provides exactly what was requested without over-generating content, making responses more focused and useful for the student seeking admission information.
The above will
result in the following output;
Origin
The concept emerged from early natural language generation systems in the 1980s and 1990s. These rule-based systems needed explicit termination conditions to prevent infinite loops or runaway generation. Researchers realized that without clear stopping criteria, automated text generators would continue producing output indefinitely. With GPT and other transformer models, stop sequences became standardized practice. The attention mechanism and autoregressive generation made stop sequences both more necessary and more effective.
Context and
Usage
Stop sequence is very useful when working on applications where you want the model to stop generating ideas once it has attained a certain number or reached a logical conclusion, such as in Q & A or dialogue-based models [2].
Why it Matters
Whether you're trying to avoid hallucinations, generate short answers or structured data, stop sequences is a powerful yet uncomplicated approach to supervising the output of LLM. Using stop sequences can successfully make your LLM application more predictable, efficient, and safe [3].
In Practice
A real-life case study of a company that makes use of stop sequences can be seen in the case of the large language model OpenAI. The stop parameter in OpenAI’s API is a feature that enables developers to define one or more sequences of characters, which when stumbled upon in the generated text, will stop the output. This means that if the model generates any of the specified sequences, it will stop producing further text at that point [4].
See Also
Tagging (Data Labelling): Annotating data for supervised learning
Temperature: Controlling randomness in generated output
Tuning (Fine Tuning or Model Tuning): Process of adjusting model parameters to optimize performance
Turing Test: Evaluating machine intelligence
References
- Kuka, V., & Bhatt, B. (2025). LLM Parameters Explained: A Practical Guide with Examples for OpenAI API in Python.
- Singh, P. (2025). Top 7 LLM Parameters to Instantly Boost Performance.
- Metric Coders. (2025). LLM Ready Text Generator.
- Zilliz. (2025). What is the stop parameter in OpenAI’s API, and how do I use it?