# Overview

> Prompt engineering is the strategic crafting of prompts to guide Large Language Models to produce accurate and desired outputs.

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## 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](/guides/prompt-engineering/be-specific-and-clear)
2. [Use structured formats](/guides/prompt-engineering/use-structured-formats)
3. [Leverage role-playing](/guides/prompt-engineering/leverage-role-playing)
4. [Implement few-shot learning](/guides/prompt-engineering/implement-few-shot-learning)
5. [Use constrained outputs](/guides/prompt-engineering/use-constrained-outputs)
6. [Use chain-of-thought prompting](/guides/prompt-engineering/use-chain-of-thought-prompting)
7. [Use thread-of-thought prompting](/guides/prompt-engineering/use-thread-of-thought-prompting)
8. [Use least-to-most prompting](/guides/prompt-engineering/use-least-to-most-prompting)
9. [Use meta-prompting](/guides/prompt-engineering/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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{" "}

<Accordion title="How often should I update my prompts?">
  Regularly: Especially if you notice changes in the model's performance or
  after updates to the model. - After use feedback: Incorporate feedback to
  improve prompt effectiveness. - When introducing a new feature: Adjust the
  prompt to cover new functionalities or use cases.
</Accordion>

{" "}

<Accordion title="What's the difference between prompt engineering vs. fine-tuning?">
  Prompt engineering is modifying the input prompts to guide the model's
  responses without changing the model itself. Fine-tuning is training the model
  on additional data to adjust its internal parameters for specific tasks.
</Accordion>

{" "}

<Accordion title="What are some common mistakes with prompt engineering?">
  - **Vague instructions**: Leads to unpredictable outputs. - **Overcomplicating
  prompts**: Too much information can confuse the model. - **Ignoring model
  limitations**: Expecting the model to perform tasks beyond its capabilities. -
  **Lack of testing**: Not validating prompts with various inputs can result in
  inconsistent performance.
</Accordion>

  <Accordion title="How does prompt length affect model responses?">
    - Short prompts can lead to ambiguous or generic answers because of a lack of context.
    - Long prompts provides more detail but can increase token usage and overwhelm the model.

    The optimal balance is aiming for concise prompts that include all necessary information without unnecessary verbosity.

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{" "}

<Accordion title="Can prompt engineering remove biases?">
  Yes. When you carefully craft prompts to avoid sensitive topics or by
  instructing the model to follow ethical guidelines, you can reduce the
  likelihood of biased or inappropriate responses.
</Accordion>

{" "}

<Accordion title="Do I need to be technical to prompt engineer?">
  - For simple prompt adjustment, no extensive technical background is needed. -
  For complex tasks or with the intention to optimize performance, some
  familiarity with AI concepts is helpful.
</Accordion>

{" "}

<Accordion title="Can you cut cost with prompt engineering?">
  Yes. A well-written prompt can minimize the number of tokens required for both
  the input and output, thereby reducing API usage costs.
</Accordion>

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