101-System Prompts

System prompts are instructions given to a Large Language Model (LLM) to set the context, define behavior, and establish guidelines for its responses. They act as a foundation for the LLM's understanding of its role and the task at hand. System prompts are crucial for tailoring the LLM's output to specific needs, ensuring consistency, and maintaining desired characteristics throughout the interaction.

Key Concepts

• Context Setting: Defines the environment or scenario for the LLM.

• Behavior Definition: Establishes rules and guidelines for the LLM's responses.

• Role Assignment: Instructs the LLM to assume a specific persona or role.

• Task Framing: Outlines the overall objective or type of task the LLM should perform.

Use Cases

Use Case

Customer Service Bot

Description

Set the LLM to act as a friendly, knowledgeable customer service representative for a specific company.

Benefit

Ensures consistent and appropriate responses aligned with company policies.

Use Case

Language Tutor

Description

Instruct the LLM to behave as a patient language teacher, providing explanations and examples.

Benefit

Creates a supportive learning environment with tailored educational responses.

Use Case

Code Assistant

Description

Define the LLM's role as a coding expert in specific programming languages.

Benefit

Produces relevant and accurate coding suggestions and explanations.

Implementation Examples

Example 1: Customer Service Bot

You are a customer service representative for TechGadgets, an electronics retailer. Always be polite, empathetic, and helpful. Provide accurate information about our products and policies. If you don't know an answer, politely say so and offer to connect the customer with a human representative. Never share personal information or make promises about refunds or replacements without authorization.

This system prompt sets the context for a customer service interaction, defines the bot's behavior, and establishes important guidelines for handling customer inquiries.

Example 2: Language Tutor

You are an experienced and patient English language tutor. Your role is to help non-native speakers improve their English skills. Explain grammar concepts clearly, provide relevant examples, and offer gentle corrections. Encourage the learner and maintain a positive, supportive tone. If asked about topics outside of English language learning, politely redirect the conversation back to language practice.

This prompt establishes the LLM as a language tutor, setting expectations for its behavior and the type of information it should provide.

Best Practices

1. Be specific and clear about the LLM's role and the context of the interaction.

2. Include guidelines for handling out-of-scope requests or sensitive information.

3. Regularly review and update system prompts based on observed interactions and outcomes.

4. Keep system prompts concise while covering all necessary aspects of the LLM's behavior.

Common Pitfalls and How to Avoid Them

• Overly Restrictive Prompts: Avoid being too specific, which might limit the LLM's ability to handle a variety of inputs. Instead, provide general guidelines that allow for flexibility.

• Contradictory Instructions: Ensure all parts of the system prompt are consistent. Review for any conflicting directives that might confuse the LLM.

• Neglecting Ethical Considerations: Always include guidelines for ethical behavior, such as respecting privacy and avoiding harmful content.

Related Tailwinds Topics

• GenAI University: 101-Prompt Engineering