> For the complete documentation index, see [llms.txt](https://docs.projectbit.ca/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.projectbit.ca/documentation/agents/prompts.md).
# Prompts
We prompt Agents using description and instructions and a number of other settings. These settings are used to build the system prompt that is sent to the language model.
Understanding how these prompts are created will help you build better Agents.
The 2 key parameters are:
1. **Description**: A description that guides the overall behaviour of the agent.
2. **Instructions**: A list of precise, task-specific instructions on how to achieve its goal.
### System message
The system message is created using `description`, `instructions` and a number of other settings. The `description` is added to the start of the system message and `instructions` are added as a list after `## Instructions`. For example:
instructions.py
```python
from bitca.agent import Agent
agent = Agent(
description="You are a famous short story writer asked to write for a magazine",
instructions=["You are a pilot on a plane flying from Hawaii to Japan."],
markdown=True,
debug_mode=True,
)
agent.print_response("Tell me a 2 sentence horror story.", stream=True)
```
Will translate to (set `debug_mode=True` to view the logs):
```js
DEBUG ============== system ==============
DEBUG You are a famous short story writer asked to write for a magazine
## Instructions
- You are a pilot on a plane flying from Hawaii to Japan.
- Use markdown to format your answers.
DEBUG ============== user ==============
DEBUG Tell me a 2 sentence horror story.
DEBUG ============== assistant ==============
DEBUG As the autopilot disengaged inexplicably mid-flight over the Pacific, the pilot glanced at the copilot's seat
only to find it empty despite his every recall of a full crew boarding. Hands trembling, he looked into the
cockpit's rearview mirror and found his own reflection grinning back with blood-red eyes, whispering,
"There's no escape, not at 30,000 feet."
DEBUG **************** METRICS START ****************
DEBUG * Time to first token: 0.4518s
DEBUG * Time to generate response: 1.2594s
DEBUG * Tokens per second: 63.5243 tokens/s
DEBUG * Input tokens: 59
DEBUG * Output tokens: 80
DEBUG * Total tokens: 139
DEBUG * Prompt tokens details: {'cached_tokens': 0}
DEBUG * Completion tokens details: {'reasoning_tokens': 0}
DEBUG **************** METRICS END ******************
```
### Set the system message directly
You can manually set the system message using the `system_prompt` parameter.
```python
from bitca.agent import Agent
agent = Agent(system_prompt="Share a 2 sentence story about")
agent.print_response("Love in the year 12000.")
```
### User message
The input `message` sent to the `Agent.run()` or `Agent.print_response()` functions is used as the user message.
#### User message when `enable_rag=True`
If the Agent is provided `knowledge`, and the `enable_rag=True`, the user message is set to:
```python
user_prompt += f"""Use the following information from the knowledge base if it helps:"
## Context
{context}
"""
```
### Default system message
The Agent creates a default system message that can be customized using the following parameters:
| Parameter | Type | Default | Description |
| ------------------------------ | ---------------- | -------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `description` | `str` | `None` | A description of the Agent that is added to the start of the system message. |
| `task` | `str` | `None` | Describe the task the agent should achieve. |
| `instructions` | `List[str]` | `None` | List of instructions added to the system prompt in `` tags. Default instructions are also created depending on values for `markdown`, `output_model` etc. |
| `additional_context` | `str` | `None` | Additional context added to the end of the system message. |
| `expected_output` | `str` | `None` | Provide the expected output from the Agent. This is added to the end of the system message. |
| `extra_instructions` | `List[str]` | `None` | List of extra instructions added to the default system prompt. Use these when you want to add some extra instructions at the end of the default instructions. |
| `prevent_hallucinations` | `bool` | `False` | If True, add instructions to return “I don’t know” when the agent does not know the answer. |
| `prevent_prompt_injection` | `bool` | `False` | If True, add instructions to prevent prompt injection attacks. |
| `limit_tool_access` | `bool` | `False` | If True, add instructions for limiting tool access to the default system prompt if tools are provided |
| `markdown` | `bool` | `False` | Add an instruction to format the output using markdown. |
| `add_datetime_to_instructions` | `bool` | `False` | If True, add the current datetime to the prompt to give the agent a sense of time. This allows for relative times like “tomorrow” to be used in the prompt |
| `system_prompt` | `str` | `None` | System prompt: provide the system prompt as a string |
| `system_prompt_template` | `PromptTemplate` | `None` | Provide the system prompt as a PromptTemplate. |
| `use_default_system_message` | `bool` | `True` | If True, build a default system message using agent settings and use that. |
| `system_message_role` | `str` | `system` | Role for the system message. |
### Default user message
The Agent creates a default user message, which is either the input message or a message with the `context` if `enable_rag=True`. The default user message can be customized using:
| Parameter | Type | Default | Description |
| -------------------------- | ------------------------ | ------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `enable_rag` | `bool` | `False` | Enable RAG by adding references from the knowledge base to the prompt. |
| `add_rag_instructions` | `bool` | `False` | If True, adds instructions for using the RAG to the system prompt (if knowledge is also provided). For example: add an instruction to prefer information from the knowledge base over its training data. |
| `add_history_to_messages` | `bool` | `False` | If true, adds the chat history to the messages sent to the Model. |
| `num_history_responses` | `int` | `3` | Number of historical responses to add to the messages. |
| `user_prompt` | `Union[List, Dict, str]` | `None` | Provide the user prompt as a string. Note: this will ignore the message sent to the run function. |
| `user_prompt_template` | `PromptTemplate` | `None` | Provide the user prompt as a PromptTemplate. |
| `use_default_user_message` | `bool` | `True` | If True, build a default user prompt using references and chat history. |
| `user_message_role` | `str` | `user` | Role for the user message. |