Prompt Engineering Beyond the Basics: Patterns That Actually Work
Beyond "Write Me a Poem"
Most prompt engineering guides stop at basic instruction-following. Real-world AI engineering requires structured techniques that produce reliable, consistent outputs at scale.
Patterns That Work in Production
Chain-of-Thought (CoT) — Ask the model to reason step-by-step before answering. This dramatically improves accuracy on complex tasks like math, logic, and multi-step analysis. Simply adding "Let's think step by step" can boost performance, but structured CoT with explicit reasoning stages works even better.
Few-Shot Examples — Provide 2-5 examples of the input/output format you expect. This is the most reliable way to control output structure without fine-tuning. The examples act as implicit instructions the model follows.
System Prompts — Set the model's role, constraints, and output format upfront. A well-crafted system prompt is the foundation of any production AI feature. Be specific about what the model should and shouldn't do.
Tool Calling — Modern LLMs can invoke external functions. Instead of asking the model to generate API calls as text, you define function schemas and the model returns structured calls. This is how agents interact with the real world.
Structured Output
For production systems, you often need JSON, not prose. Techniques include:
- Defining JSON schemas in the system prompt
- Using function calling / tool use for guaranteed structure
- Pydantic models with OpenAI's structured output mode
- Output parsers that validate and retry on failure
Prompt Security
Prompt injection is a real threat. Users can craft inputs that override your system prompt. Defense strategies include input sanitization, output validation, and separating user input from system instructions.
The Key Insight
Prompt engineering is software engineering. Treat prompts as code: version them, test them, measure their performance, and iterate based on data.