Behind the prompt: Prompting tips for Claude.ai

foreign I'm Alex, I'm a prompt engineer at Anthropic. I help people get the most out of Claude with safety at the top of mind. I first got into prompt engineering back in last August. Anthropic released their paper, "Red Teaming Language Models to Reduce Harms" and immediately I read it and it was hooked.

I was inspired to see that a company was taking a safety first approach to researching language models, and I thought it was really interesting how you could see the ways that models would output to different and diverse ranges of prompts. You may be familiar with red teaming attacks as prompt exploits or the more Infamous name "jailbreaks". I decided to start writing jailbreaks after reading the paper and becoming inspired by the opportunities that still existed to red team these models. Jailbreaks are specific prompts that are written to circumvent the filters that have been applied on top of language models. Prompt engineering is the practice of optimizing your prompt in order to get the best response from the language model. At anthropic we like to take an

empirical test driven approach to prompt engineering. Whenever we write a new prompt we run it against a series of benchmarks in order to scientifically measure its performance. With Claude we've discovered a set of best practices that'll allow you to get the most out of the model. So, let's get into it here are my five tips for getting the best performance from Claude. First, describe your task. Claude responds well to clear direct and specific instructions. Let's say you wanted Claude to remove personal identifiable information from a piece of text. Explaining the quad exactly what that means helps Claude recognize what pieces of text to remove. For example email addresses and phone numbers. Second, mark different parts of your prompt with XML tags look like this. Claude has been fine-tuned to pay special attention to their structure. In our example we use XML tags to indicate the beginning and end of text that claw needs to de-identify. Third, give examples. The more examples

the better. Including a wide range of examples helps Claude learn how to do the task. Back to our PII prompt, we provide cod with examples of how to de-identify text within XML tags. Fourth, make use of the long context.

Claude can read up to a hundred-thousand tokens. That's roughly 70 000 words or the length of the entire Great Gatsby. And finally, the last tip is to let

Claude think. Researchers have discovered that giving language models some time to think through their response before producing their final answer leads to better performance. With Claude, we like to use thinking tags so that it can jot down its ideas before answering a complex question. Here in this example you can see Claude starts to reason within thinking tags and then outputs its final answer.

Alright, so those are my top tips for getting the most out of Claude and a little bit about me and my own prompting Journey. To stay up to date on the latest prompting best practices, make sure to go check out our developer docs site. And if you haven't got access to the Claude API yet, you can still practice your prompt engineering right now at claude. ai