Three prompting techniques..

1. Meta-Prompting: “Prompt about the prompt”

Type: Prompt-optimization technique.
Goal: Ask the AI to improve or refine your prompt before you actually use it.
Idea: Instead of guessing how to ask a question best, you ask the model to help you craft the best version of that question.

Example use: “Help me rewrite my prompt to get more detailed and creative UX analytics ideas.”

You use it when:

• You’re unsure how to phrase your question.

• You want to generate multiple strong prompts quickly.

• You want to get the most out of high-end reasoning models.

2. Few-Shot Prompting: “Show, don’t tell”

Type: Demonstration-based technique.
Goal: Teach the model by showing a few examples of what you want before asking for a new output.
Idea: Like showing a student sample answers before giving them a test.

Example use: Provide 2–4 examples of input/output pairs so the model learns the desired style, tone, or structure.

You use it when:

• You need consistent tone or structure (e.g., formal report vs. casual rewrite).

• You want the model to “learn” quickly what good looks like.

• You want precision and minimal guesswork.

3. Chain-of-Thought Prompting “Think step by step”

What is it?
A technique where you ask the model to “think out loud” by breaking its reasoning into smaller steps before giving a final answer.

Why is it helpful?

• Improves performance on complex reasoning tasks, like solving multi-step problems or equations.

• Useful for uncovering the model’s reasoning path, which makes it easier to validate or correct.

When should you use this approach?

• When solving multi-step problems (e.g., math, logic puzzles, or structured decision-making).

• When you need to audit or verify how the model arrived at its answer.