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Javier Lacort
Senior WriterI write long-form content at Xataka about the intersection between technology, business and society. I also host the daily Spanish podcast Loop infinito (Infinite Loop), where we analyze Apple news and put it into perspective.
Karen Alfaro
WriterCommunications professional with a decade of experience as a copywriter, proofreader, and editor. As a travel and science journalist, I've collaborated with several print and digital outlets around the world. I'm passionate about culture, music, food, history, and innovative technologies.
Anthropic, the company behind Claude, has published its definitive guide to prompt engineering. This free guide synthesizes years of research into practical techniques for maximizing the benefits of Claude.
Why it matters. Most users don’t realize the full capabilities of generative AI, which means they often miss results that could enhance their everyday tasks—even outside of engineering.
The overview. The guide ranges from basic to advanced techniques. It covers everything from how to be clear and direct in a request to more sophisticated methods such as multishot prompting and thought chains.
It culminates in strategies like chaining complex prompts—with real examples from Claude.
In detail. The core techniques in this guide work like a ladder.
- Clarity and direction. Say precisely what you want. Vague prompts produce vague results.
- Provide various examples. Show Claude how to think by presenting multiple use cases.
- Chains of thought. Ask Claude to think step by step before answering.
- XML tags. Structure the answers more precisely.
- System roles: Use the classic “You’re a lawyer,” “You’re a data analyst,” etc., to change the chatbot’s perspective.
- Pre-fill responses. Guide the tone and format by starting your response.
Context. According to Anthropic, companies can solve many problems with generative AI by using better prompts rather than building more powerful models.
This guide aims to democratize techniques previously known only to advanced developers. It pulls back the curtain on what works.
The guide includes concrete examples:
- Instead of “Create a dashboard,” write: “Create an analytics dashboard complete with interactive graphs and filters.”
- For front-end code, add specific modifiers: “Include smooth transitions, micro-interactions, and visual effects that demonstrate advanced web development capabilities.”
- For complex tasks, the guide recommends using XML tags—such as
<step-by-step_analysis>—to structure Claude’s thinking.
The difference lies in specificity. Clear instructions lead to significantly better results. AI reflects what you put into it.
Yes, but. There’s an important nuance: The guide assumes you already have clear success criteria and methods for evaluating results.
Without that foundation, even the best prompt is a long shot.
So, what’s next? For developers, this guide is invaluable. For casual users, mastering even the first few techniques can improve outcomes and reduce confusion.
Prompt engineering, once seen as black magic, is now a learnable, measurable, and improvable skill.
Image | BoliviaInteligente (Unsplash)
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