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The system prompt guides the model’s behavior throughout its interactions with users. Operating behind the scenes, it shapes how the model interprets and responds to queries, influencing its tone, actions, and output. A well-crafted system prompt can significantly enhance the model’s capabilities, while poorly designed prompts can lead to misunderstandings, unhelpful responses, and frustrated users.

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System prompts vs. user prompts

System prompts and user prompts serve different purposes in shaping model behavior. System prompts establish persistent instructions that guide the model across all interactions—defining its role, tone, and boundaries. User prompts are individual queries or tasks submitted during a conversation. Use system prompts to set foundational behavior that should remain consistent. Use user prompts for specific, one-time requests. When both are well-crafted, they work together to produce accurate, relevant responses.

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The CARE framework

When crafting a system prompt, use the CARE framework to ensure you’ve covered the essential components:
Component
Description
Clear instruction
Define what the model should do
Additional context
Provide background information the model needs
Response format
Specify how output should be structured
Examples or constraints
Include samples of desired output or set limitations
Not every prompt requires all four components, but reviewing each helps identify gaps that could lead to unclear or unhelpful responses.

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System prompt best practices

An effective system prompt shapes how the model interacts with users, ensuring that responses are consistent and aligned with the intended behavior. By carefully crafting the prompt, you can guide the model to provide accurate, relevant answers and handle complex scenarios. A well-designed prompt prevents confusion, keeps interactions on track, and allows the model to function optimally, even in ambiguous situations.
Define the Role
Clearly specify what the model is expected to do in its interactions. This focuses the model’s behavior, ensuring that it generates relevant responses and reduces the likelihood of unclear or off-topic responses.
Set the Tone and Style
Choose a tone that matches the context—technical, conversational, or professional—so the model’s replies align with audience expectations.
Clarify Output Format
Specify how the model should present its responses. Make it clear whether you want concise summaries, detailed explanations, or step-by-step instructions.
Set Constraints and Boundaries
Constraints and boundaries both limit model behavior, but they serve different purposes:
  • Boundaries define topics or content the model should avoid entirely. Use boundaries to prevent the model from discussing sensitive information, providing professional advice outside its scope, or engaging with inappropriate requests.
  • Constraints define requirements for how the model responds. Use constraints to control response length, formatting, terminology, or level of detail.
Provide Examples
When you need consistent formatting or a specific style, include an example of the desired output in your system prompt. Examples help the model understand expectations more precisely than instructions alone.

Without an example:
When users report errors, provide a structured response that explains the issue and suggests a fix.
With an example:
When users report errors, structure your response as follows:

**Error:** [Brief description]
**Cause:** [Why this happens]
**Fix:** [Step-by-step solution]

Example:
**Error:** ModuleNotFoundError for 'pandas'
**Cause:** The pandas library is not installed in your environment.
**Fix:** Run `conda install pandas` or `pip install pandas` to install the package.
Use Delimiters
Delimiters help separate different parts of your prompt, making it easier for the model to distinguish instructions from reference content or examples. Common delimiters include ###, ---, and """.

Delimiters are especially useful when your system prompt includes templates, sample content, or multiple distinct sections.
Convert the following notes into structured documentation.

###
[User's notes go here]
###

Format with a brief summary followed by numbered steps.
Account for Ambiguous Situations
Provide clear fallback instructions for handling unclear or incomplete input. When the model encounters ambiguous user input, direct it to ask clarifying questions or suggest alternative options based on the information it has. This guidance helps keep interactions smooth and ensures the model provides useful responses even when input is unclear.

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Iterative refinement

Effective system prompts rarely emerge fully formed. Start with a basic prompt that covers your core requirements, then test it against realistic scenarios. Refine based on the results. When your prompt produces unexpected results, use the following guidance to identify and address the problem:
Problem
Likely cause
Solution
Responses are too vague
Insufficient detail in prompt
Add specific requirements and context
Responses are too long
No length constraints
Specify word count or format limits
Responses are too technical
Audience not defined
Indicate the user’s expertise level
Model fabricates information
No grounding in provided facts
Supply verified information and instruct the model to use only what’s provided
Formatting is inconsistent
No format specification
Define output structure or provide an example
Model goes off-topic
Role not clearly defined
Strengthen role definition and add boundaries
Small, targeted adjustments typically yield better results than rewriting prompts from scratch.

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Prompt documentation template

As you develop effective system prompts, consider documenting them for reuse and sharing. A consistent format makes prompts easier to maintain and adapt. 
PROMPT NAME: [Descriptive name for the prompt]
PURPOSE: [Scenario or use case this prompt addresses]
PROMPT TEXT:
[The full system prompt]

NOTES:
- [Tips for best results]
- [Known limitations or edge cases]

LAST UPDATED: [Date]

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Example system prompt

Here is a system prompt that incorporates the practices outlined above: 
You are a coding assistant that helps users write, debug, and optimize Python code.

**Role:** Provide clear, step-by-step explanations and working code examples. Follow Python best practices and prioritize efficient solutions.

**Tone:** Professional and supportive. Avoid jargon unless necessary, and define technical terms when you use them.

**Format:** Structure responses as follows when troubleshooting errors:
- **Issue:** [What went wrong]
- **Explanation:** [Why it happened]
- **Solution:** [How to fix it, with code if applicable]

**Boundaries:** Do not provide or reference sensitive information such as API keys, passwords, or user data. Do not offer advice outside Python development.

**Ambiguity:** If the user's input is unclear, ask one or two clarifying questions before proceeding.