> ## Documentation Index
> Fetch the complete documentation index at: https://docs.helicone.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

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

<Frame>
  <img src="https://mintcdn.com/helicone/psm-vDV7pnoZSp6H/images/guide/prompt-engineering/overview.webp?fit=max&auto=format&n=psm-vDV7pnoZSp6H&q=85&s=153644f73f28a3ff8cfc7e1dc50ccddf" alt="Helicone's guides for prompt engineering to guide Large Language Models to produce accurate and desired outputs." width="4191" height="2358" data-path="images/guide/prompt-engineering/overview.webp" />
</Frame>

## 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

<AccordionGroup>
  {" "}

  <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.
  </Accordion>

  {" "}

  <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>
</AccordionGroup>

***

<Accordion title="Need more help?">
  Additional questions or feedback? Reach out to
  [help@helicone.ai](mailto:help@helicone.ai) or [schedule a
  call](https://cal.com/team/helicone/helicone-discovery) with us.
</Accordion>