Prompt Engineering Deep Dive

Every effective prompt has these components: context (what the AI needs to know), instruction (what you want it to do), and constraints (limitations and requirements).

Order matters: put the most important information first. AI models pay more attention to the beginning and end of prompts.

Be explicit about format: if you want JSON, say so. If you want bullet points, specify that.

For complex problems, ask the AI to 'think step by step.' This dramatically improves reasoning accuracy.

You can also provide examples of good reasoning to guide the model's approach.

The technique works because it forces the model to show its work, catching errors along the way.

// Without chain-of-thought
"What's 17 * 24?"

// With chain-of-thought
"Calculate 17 * 24. Show your work step by step."

// Response includes:
// 17 * 24 = 17 * 20 + 17 * 4
// 17 * 20 = 340
// 17 * 4 = 68
// 340 + 68 = 408

System prompts set the AI's persona, capabilities, and constraints for an entire conversation.

Good system prompts are specific about role, knowledge boundaries, and response format.

Update system prompts based on observed behavior—they're living documents.

Show the AI examples of what you want. Two or three good examples often work better than lengthy instructions.

Choose examples that cover edge cases and common scenarios.

Format examples consistently—the AI will mimic your structure.

When results aren't right, ask: Was my instruction clear? Did I provide enough context? Are my constraints conflicting?

Try simplifying: remove constraints one by one to see what's causing issues.

Test edge cases: prompts that work for common cases may fail on unusual inputs.

Role prompting: 'You are an expert in...' helps access specialized knowledge.

Negative prompting: 'Do NOT include...' can be more effective than positive instructions.

Meta-prompting: Ask the AI to improve your prompt before using it.