# Prompt Template
## Desription
Prompt Template.
## Code Example
### Example
```python
from towhee import ops, pipe
import requests
towhee_docs = requests.get('https://raw.githubusercontent.com/towhee-io/towhee/main/README.md').content
temp = """{question}
input:
{context}
"""
sys_message = """Your name is TowheeChat."""
p = (
pipe.input('question', 'doc', 'history')
.map('doc', 'doc', lambda x: x[:2000])
.map(('question', 'doc', 'history'), 'prompt', ops.prompt.template(temp, ['question', 'context'], sys_message))
.map('prompt', 'answer', ops.LLM.OpenAI())
.output('answer')
)
an1 = p('What is your name?', [], []).get()[0]
print(an1)
an2 = p('Tell me something about Towhee', towhee_docs, []).get()[0]
print(an2)
an3 = p('How to use it', towhee_docs, [('Tell me something about Towhee', an2)]).get()[0]
print(an3)
```
## Factory Constructor
Create the operator via the following factory method:
***ops.prompt.template(temp, keys, sys_msg)***
**Parameters:**
***temp***: *str*
A template to create a prompt as the last user message.
***keys***: *list*
A list of keys used in template.
***sys_msg***: *str=None*
A system message, defaults to None. If None, it will not pass any system message.
**Returns:** *List[Dict]*
# More Resources
- [ChatGPT+ Vector database + prompt-as-code - The CVP Stack - Zilliz blog](https://zilliz.com/blog/ChatGPT-VectorDB-Prompt-as-code): Extend the capability of ChatGPT with a Vector database and prompts-as-code
- [What is Prompt as Code (Prompt Engineering)](https://zilliz.com/glossary/prompt-as-code-(prompt-engineering)): Explores what prompt engineering is, how it works in NLP, and best practices for effective prompt engineering.
- [An LLM Powered Text to Image Prompt Generation with Milvus - Zilliz blog](https://zilliz.com/blog/llm-powered-text-to-image-prompt-generation-with-milvus): An interesting LLM project powered by the Milvus vector database for generating more efficient text-to-image prompts.
- [Prompting in LangChain - Zilliz blog](https://zilliz.com/blog/prompting-langchain): Prompting is one of today's most popular and important tasks in AI app building. Learn how to use LangChain for more complex prompts.
- [What is Prompt Chaining](https://zilliz.com/glossary/prompt-chaining): Prompt chaining in NLP uses structured prompts to break a complex task into smaller subtasks. This sequential approach improves coherence and accuracy in LLM outputs.