Direct instruction without examples. Ask the model to perform a task relying solely on its training.
Provide examples in the prompt to demonstrate the desired pattern or format.
Guide the model to show intermediate reasoning steps before reaching a conclusion.
Assign the model a specific role or expertise to shape tone, depth, and perspective.
Click a strategy to see how it evolved
and when to apply it today
Zero-Shot Prompting
Often insufficient for complex tasks. Models needed examples to understand what was expected. "Just ask" frequently produced inconsistent results.
Strong models handle zero-shot well for most tasks. Instruction-tuning and RLHF have made models much better at understanding intent without examples.
The Enduring Principle
Clarity and specificity in your instruction matter more than tricks. The better you articulate what you want, the better the output—regardless of technique.
When to Apply Today
Few-Shot Prompting
Essential for complex tasks. In-context learning was a breakthrough—providing examples unlocked capabilities models couldn't otherwise demonstrate.
Primary function is output format alignment—showing the model exactly how you want results structured. Less about unlocking capability, more about shaping presentation.
The Enduring Principle
Showing is often clearer than telling. When you need a specific pattern, format, or style, a concrete example communicates more than description alone.
When to Apply Today
Chain of Thought
Breakthrough for reasoning tasks. "Let's think step by step" dramatically improved math, logic, and complex problem-solving. Required explicit prompting.
Reasoning-native models (o1, Claude with extended thinking) have CoT built in. Explicit prompting less necessary—but still useful for transparency and verification.
The Enduring Principle
Breaking problems into steps improves accuracy. Whether you prompt for it or the model does it internally, structured reasoning produces better outcomes on complex tasks.
When to Apply Today
Role / Persona
"Act as a senior engineer" or "You are an expert in X" was seen as unlocking hidden knowledge. Viral prompt templates featured elaborate role assignments.
Still effective for framing tone, depth, and perspective—but the model isn't "becoming" the persona. It's adjusting register based on context you've provided.
The Enduring Principle
Context shapes output. Establishing who the "speaker" is and who the "audience" is focuses the response—just like framing works in human communication.