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

# Prompting Tips

> Prompts are the instructions given to AI models. Output quality depends on how well they are written for each model.

## Top tips for getting the most out of AI models like Claude 3.5 Sonnet and GPT-4o

1. **Be specific and clear:** Provide clear, explicit instructions with all the necessary details.
2. **Provide context:** Include any necessary background information for your question. AI models typically have broad knowledge so any specifics should be provided in the context.
3. **Describe persona:** Instruct AI to pretend to be a character or role, or take on a persona. This is useful shorthand to guide the model. Example: "You are a product manager".
4. **Set format expectations:** Define the desired output structure (e.g., bullet points, markdown, JSON). Be aware that asking for too much structure has been shown to reduce reasoning ability.

> **Tip:** Add "Use well-structured markdown" to your prompt to add headings, bullets, and more.

5. **Use examples for clarity:** Include a few examples in your instructions like pairs of representative inputs and outputs. This will help guide the model towards the type of answer you expect. Prompt engineers call this "few-shot prompting" — prompting without examples is called "one-shot prompting".
6. **Prompt step by step:** Specifically insert “Think step by step” (for OpenAI's models) or "Think step by step in <code>\<thinking></code> tags" (for Anthropic's models).
7. **Use a variety of AI models:** Different models can handle different tasks like writing, analysis, creative, math, and coding. Use all the top models together in Hunch.
8. **Flow of thought:** Break complex tasks down into multiple sub-tasks and "chain" them together. Hunch excels at this! In Hunch each sub-task is a block, and blocks are connected together on a canvas to achieve a more complex task.
9. **Iterate and refine:** This applies to all the steps above. If the initial response isn't what you expected, refine your prompt and try again.
10. **Understand limitations:** The AI model itself often doesn't have access to external data or the ability to browse the web for real-time info. Custom tools in Hunch can help with these as well!

> **General tip:** Think of AI as a very capable intern.

Effective prompting will involve a combination of these strategies to get the most relevant and high-quality responses.

## Example task

Prompting [suggestion](https://www.anthropic.com/news/prompt-engineering-for-business-performance) from Anthropic: "For example, if you want Claude to help with explaining tax situations, you could first prompt it to create a list of the tax codes that are related to the specific question, then prompt Claude to identify the relevant sections in each document, and finally, to respond to a user question based on the information Claude's gathered."

Each of these steps would be AI blocks in Hunch. You don't even have to use the same model for each step — you might discover that GPT-4o is better for some, or you can accelerate the flow with Claude 3 Haiku.

> **Bonus advanced tip:** Claude likes XML tags in prompts — it's a great way to identify examples and other context for Claude.

## Resources

* [OpenAI prompting guide](https://platform.openai.com/docs/guides/prompt-engineering)
* [Anthropic prompting guide](https://docs.anthropic.com/claude/docs/prompt-engineering)