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

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):

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.

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:

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 <instructions> 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.