The Anthropic Guide: Unlock Elite AI Outputs with This 10-Step Prompting Framework

Do you ever feel like your AI assistant, Claude, delivers responses that are just shy of “excellent”? You ask a question, but the answer feels surface-level, lacks depth, or comes back in a messy format, forcing you to spend time tweaking and re-prompting to get it right.

The issue might not be the model’s capability, but how you’re communicating with it. Recently, Anthropic, the creator of Claude, released an internal masterclass on prompt engineering. It’s a systematic breakdown of how to conduct efficient, precise conversations with Claude to consistently achieve elite-level outputs.

This guide isn’t a collection of scattered tips; it’s a complete, validated 10-step structured framework. Whether you use Claude for complex analysis, daily office work, or bulk processing, mastering this framework will transform your AI experience. This article will delve into this official guide, walking you through how to build professional-grade prompt engineering skills, step by step.

Step 1: Model Selection – Align Capability with Task

Before constructing your prompt, choosing the right Claude model is the foundation of success. Anthropic’s 4.5 model family has different specializations. Your choice should be based on the core needs of the task: are you chasing ultimate intelligence, or balancing speed and cost?

  1. Claude Opus 4.5: The Peak of Intelligence. This is Anthropic’s most powerful model, designed for tasks that require genuine intelligence. If your work involves complex reasoning, deep analysis, advanced coding, or creative generation, Opus is the undisputed choice. It understands the most nuanced instructions and demonstrates exceptional coherence in multi-step tasks.

  2. Claude Sonnet 4.5: The Balanced Workhorse. For the vast majority of everyday tasks, Sonnet offers the best balance. It possesses strong reasoning capabilities but is faster and less costly than Opus. Whether drafting emails, summarizing documents, conducting market analysis, or generating content drafts, Sonnet is a reliable and efficient “workhorse.”

  3. Claude Haiku 4.5: The Speed Demon. This is the fastest and most economical option. If you need to handle high-volume, straightforward tasks like data cleaning, basic categorization, quick information extraction, or instant Q&A, Haiku completes them at lightning speed. Many users prefer using Haiku in browser extensions for seamless, rapid assistance.

Core Recommendation: Don’t blindly opt for the strongest model. Dynamically selecting the model based on task complexity and your requirements for response speed and cost is the first sign of professional use.

Decoding Anthropic’s Official 10-Step Prompt Structure

With the model chosen, we move to the core. Anthropic’s framework breaks down a high-quality interaction into ten optional but highly recommended components. They are like puzzle pieces; the more complete the combination, the clearer and more precise the final output.

1. Task Context – Set the Stage Clearly

This is arguably the most critical part of any prompt. Here, you define the “role” the AI needs to play and the macro “task” at hand. This establishes the contextual boundaries for all subsequent instructions.

Example: Suppose you need to analyze a market report.

  • Weak Prompt: “Summarize this report.”
  • Strong Prompt (Task Context): “You are a Chief Market Strategist with 10 years of experience. Your task is to review our latest Q3 industry competitive analysis report and extract the core insights that will influence next year’s business decisions.”

2. Tone Context – Define the Communication Style

How do you want Claude to communicate with you? A formal, boardroom-ready tone, or a friendly, casual colleague’s voice? Clear tone instruction ensures the output aligns with your usage scenario.

Example (Combined with Task Context):
“You are a Chief Market Strategist with 10 years of experience, presenting to the company’s executive team. Your communication style needs to be professional, confident, and straight to the point, avoiding overly academic jargon.”

3. Background Data – Provide the Necessary “Fuel”

For tasks requiring deep, accurate answers, you must supply Claude with relevant background materials. This can be uploaded PDFs, TXT documents, data snippets, or a detailed contextual description.

Example: “Please use the uploaded ‘2024 Global New Energy Vehicle Market Whitepaper.PDF’ and the internal company sales data sheet to execute the above analysis and presentation task.”

4. Detailed Task Description & Rules – Draw the Precise Roadmap

Now, you need to elaborate on what specifically needs to be done and set clear constraints and guidelines. This step is key to translating macro goals into actionable steps.

Example:
“Please complete the following analysis:

  1. Identify the top three market trends mentioned in the whitepaper.
  2. Compare our sales data against these trends, pointing out where our business aligns and where gaps exist.
  3. Based on the gaps, propose three specific, actionable strategic recommendations.
    Rules:
  • Each recommendation must be supported by data.
  • Avoid using vague terms like ‘might’ or ‘could.’
  • Keep the analysis under 800 words.”

5. Examples – Show the Desired “Sample”

This is a secret to significantly improving output quality. If you have an ideal output format or style in mind, provide one or several examples directly for Claude to reference. The official guide suggests using the <example> tag to wrap your sample.

Example:
“Please draft the strategic recommendations in the style and format of the following example:
<example>
Recommendation 1: Accelerate Localized Partnerships in Southeast Asia
Rationale: The report indicates the Southeast Asian market is growing at over 30% annually, while our channel coverage in the region is only 15%.
Action Plan: Establish pilot partnerships with the top three local distributors before Q4, with an initial budget of approximately XX万元.
Expected Impact: Projected to increase channel coverage to 30% within 6 months, generating approximately YY万元 in incremental revenue.
</example>“

6. Conversation History – Continue the Previous “Memory”

If you are engaged in a multi-turn conversation with Claude on a complex topic, you can actively instruct it to reference prior discussions. This is crucial for maintaining contextual coherence. You can state it directly or use a tag like <HISTORY>.

Example: “Please recall our previous three conversations on ‘Customer Loyalty Program Optimization,’ particularly the user feedback data mentioned, and then based on that…”

7. Immediate Task Description – Issue the Clear “Action Order”

This is different from the macro task context in Step 1. It specifically refers to the action you want the AI to execute in this immediate conversation. Using strong verbs to start is a key technique here.

Example (Building on the previous steps):
“Based on all the above context, data, and rules, now, please:

  1. List the three major market trends.
  2. Create a comparative analysis table.
  3. Draft the executive summary for the presentation.”

8. Deep Thinking – Trigger the Model’s Reasoning Engine

For complex or challenging tasks, explicitly asking Claude to “think deeply” can significantly enhance the accuracy and logical rigor of its output. This simple instruction prompts the model to engage deeper reasoning capabilities rather than providing a quick but superficial answer.

A comparison chart in the official guide clearly shows that when dealing with mathematical or logical reasoning, adding a “Think Deeply” prompt resulted in the model not only giving the final answer but also displaying the complete step-by-step reasoning process, greatly increasing trustworthiness.

9. Output Formatting – Specify the Desired “Container”

Before sending your prompt, pre-defining the output format you want can save a lot of time on post-processing. You can request specific structures.

Example: “Please present the final report in the following format:

  • Title
  • Key Findings (3-4 bullet points)
  • Data Comparison Table
  • Strategic Recommendations (listed with each containing: Recommendation, Rationale, Action Steps)”

10. Prefilled Response – Provide a Structured “Opening”

This is not mandatory but can be the icing on the cake. You can provide a starting framework for Claude’s response, guiding it to organize information in a specific structure.

Example: “Please begin your response with the following opening:
‘Dear Executives,
Based on the analysis of the latest market report, my presentation will cover three areas: trend insights, our company’s current status, and strategic recommendations. First, we have observed three key trends…'”

How to Apply the 10-Step Framework in Practice: From Theory to Action

Looking at this 10-step framework, your first reaction might be: “Do I need to write this much for every query? That’s too time-consuming.”

Indeed, not every simple query requires assembling all the “puzzle pieces.” However, the true value of this framework lies in providing a systematic way of thinking. For important, complex tasks, following this structure ensures thoroughness. For simple tasks, you can flexibly simplify it, but the core logic remains: provide clear context, explicit instructions, and expected format.

Automating Your Prompt Engineering Workflow

To use this framework efficiently, the official guide suggests automation. Here are two ideas:

  1. Build a Prompt Generator: You could create a simple tool (website, app, or dashboard) where a user inputs a basic instruction (e.g., “Help me write a sales email”), and the tool automatically expands it into a structured, detailed professional prompt following the 10-step framework.
  2. Create an LLM Prompt Conversion Project: Feed this guide itself as a context file into a Claude project. Afterwards, whenever you send a brief idea to this project, it can automatically convert it into a detailed prompt adhering to the 10-step framework.

Toolkit of Practical Resources

In addition to the core framework above, Anthropic provides a series of official resources to help you deepen your prompt engineering skills:

  • Anthropic Official Prompt Engineering Overview Docs: Understand foundational principles and best practices.
  • Anthropic Interactive Prompt Engineering Tutorial: Learn through hands-on practice on GitHub.
  • Anthropic Prompt Engineering PDF Guide: A downloadable, detailed reference document.
  • Anthropic Official Prompt Library: View high-quality prompt examples for various scenarios (e.g., writing, analysis, coding).
  • Awesome Claude Prompts Community Library: An open-source project collecting a vast number of excellent prompts for reference and inspiration.

Frequently Asked Questions (FAQ)

Q1: Do I have to use all 10 steps every single time?
A1: Absolutely not. It is a “toolbox,” not a “checklist.” For simple queries (e.g., “translate this sentence”), you might only need the “Immediate Task Description” and “Output Formatting.” For complex projects (e.g., “develop a strategy based on this 100-page report”), you should apply as many of these steps as possible for optimal results.

Q2: How exactly do I provide the “Examples”? Do I need to give a complete output sample?
A2: A complete sample isn’t always necessary. You can provide snippets of the desired style, examples of paragraph structure, or an outline of an ideal answer. The key is to help the model understand the format, depth, and style you expect. Using the <example> tag helps clearly distinguish it from the instructions themselves.

Q3: Is the “Deep Thinking” instruction equally effective for all models?
A3: This instruction aims to trigger the model’s deeper reasoning chain. More powerful models (like Opus) typically show a more significant response to this instruction, demonstrating more complex step-by-step reasoning. Even when used with Sonnet and Haiku, it can help them focus more on logical deduction rather than just pattern matching.

Q4: How do I reference “Conversation History”? What if the conversation is very long?
A4: You can directly instruct it to “reference our previous discussion on topic XX,” and Claude will automatically access the context within that chat window. For extremely long conversations, you can be more specific in your instruction: “reference the three conclusions we drew about user pain points in this morning’s conversation.” This is more precise than hoping the model sifts through the entire history itself.

Conclusion: From Random Queries to Structured Collaboration

Mastering Anthropic’s 10-step prompting framework means your relationship with Claude will evolve from “random Q&A” to “structured collaboration.” You are no longer inputting questions by chance but acting like a professional director, providing this powerful AI actor with a complete script, character background, emotional direction, and stage setting.

Ultimately, this will bring you more consistent, deeper, and more directly usable outputs, truly unleashing the vast potential of large language models in professional fields. Now, try using this framework to reconstruct your next complex prompt and experience the distinctly different elite-level output for yourself.