Prompt Engineering for Production LLM Apps
System prompt design, few-shot examples, output formatting, retry logic, cost optimization.
Prompting in Production
In real apps, prompts are code: they need structure, testing, and reliability. Good prompt engineering reduces hallucinations, enforces output format, and controls cost. This lesson covers the techniques that matter in production.
System Prompt Design
The system prompt sets the model's role, tone, constraints, and rules. Be specific: state what the assistant is, what it must and must not do, and the format of its answers. A clear system prompt is your strongest steering tool.
system = (
"You are a support agent for an e-commerce store. "
"Answer only questions about orders and shipping. "
"If asked anything else, politely decline. "
"Keep replies under 3 sentences."
)All lessons in this course
- OpenAI API: chat.completions and Streaming
- Anthropic Claude API in Python
- Function Calling and Tool Use with LLMs
- Prompt Engineering for Production LLM Apps