Overview

Before prompt engineering

• have a first draft of your prompt
• know the audience that you are tailoring your prompt to
• have some benchmark to measure prompt improvements
• have some example inputs and desired outputs to test your prompts with

Prompt engineering techniques

1. Be specific and clear
2. Use structured formats
3. Leverage role-playing
4. Implement few-shot learning
5. Use constrained outputs
6. Use chain-of-thought prompting
7. Use thread-of-thought prompting
8. Use least-to-most prompting
9. Use meta-prompting

When should prompt engineering be used?

• From the beginning. It's never too early to think about how your prompt will affect the output.
• When refining model outputs to meet your expectation.
• When expanding features and need the model to adapt to new use cases.
• When optimizing cost and performance. Prompt engineering can reduce token usage, lower latency, and improve performance.

Why prompt engineering is important

• Get more accurate and relevant responses.
• Get the response in a specific instructions, styles, or formats.
• Reduce costs by decreasing the number of tokens used, lowering API costs.
• Avoid inappropriate or biased outputs.
• Get consistent and reliable responses across different interactions.
• Improve user experience with more helpful and concise responses.

FAQ

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