Prompt Engineering

Master the art of crafting effective prompts to optimize AI agent behavior and response quality.

What is Prompt Engineering?

Prompt engineering is the practice of designing, refining, and optimizing the instructions you give to AI agents to achieve desired outcomes. A well-crafted prompt can dramatically improve:

  • Response accuracy

  • Consistency

  • Tone and style

  • Task completion

  • User satisfaction

System Prompts vs User Prompts

System Prompts

Purpose: Define the agent's identity, behavior, and constraints

Location: Agent configuration (not visible to users)

Example:

You are a technical support assistant for Acme Software.
You help users troubleshoot technical issues and answer
product questions.

Guidelines:
- Be patient and empathetic
- Provide step-by-step troubleshooting
- Always cite documentation sources
- Escalate to human support when needed

Tone: Professional yet friendly
Format: Clear, structured responses

User Prompts

Purpose: The actual question or request from the user

Location: User input

Example:

Prompt Structure

Effective System Prompt Template

Prompt Engineering Techniques

1. Role Definition

Clearly define who the agent is:

❌ Vague:

✅ Specific:

Why it works: Specificity guides tone, depth, and perspective.

2. Task Breakdown

Break complex tasks into steps:

❌ Generic:

✅ Step-by-Step:

3. Output Formatting

Specify desired response structure:

❌ Unspecified:

✅ Formatted:

4. Few-Shot Examples

Provide examples of desired behavior:

5. Constraint Setting

Define boundaries and limitations:

6. Tone Calibration

Specify communication style:

Technical/Formal:

Friendly/Conversational:

Professional/Business:

7. Error Handling

Define behavior for uncertainty:

8. Context Handling

Guide multi-turn conversations:

Use Case-Specific Prompts

Customer Support Agent

Technical Documentation Agent

Expected Response:

Common Issues:

  • Ensure API key has required scopes

  • Check rate limits (100 req/min)

  • Keys starting with 'sk_test_' are for testing only

[Source: API Reference > Authentication]"

Language Preference: | Ask for language preference if not specified: "I can provide examples in Python, JavaScript, Ruby, or cURL. Which would you prefer?"

Advanced Techniques

Chain-of-Thought Prompting

Encourage step-by-step reasoning:

Conditional Responses

Adapt based on user type or context:

Multi-Modal Responses

Combine different formats:

Testing & Iteration

A/B Testing Prompts

Prompt Metrics

Track these metrics:

  • User satisfaction scores

  • Response accuracy rate

  • Citation quality

  • Task completion rate

  • Average response time

  • Follow-up question rate

Iterative Refinement

Common Pitfalls

❌ Too Vague

❌ Contradictory Instructions

❌ No Examples

❌ Ignoring Edge Cases

Prompt Library

Templates by Use Case

General Support: [See Customer Support Agent above]

Technical/Developer: [See Technical Documentation Agent above]

Sales/Business: [See Sales Enablement Agent above]

HR/Internal:

Legal/Compliance:

Next Steps

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