Summary on the Anthropic Prompt Engineering

on 3 days ago

Summary on the Anthropic Prompt Engineering

I've just gone through Anthropic's May 2025 Prompt Engineering Guide. Here's my personal, no-frills summary of the most useful tips—the ones that truly make Claude work better.

I. Getting Started: Laying the Groundwork

Like preparing your tools before a job, the same applies to prompts.

Know What You Want: Many people complain Claude isn't good, but often they don't even know what they want the AI to do. You need to be clear about what a successful output looks like – is the format correct, is the content accurate, etc.?
How to Check: Once you set standards, you need a way to check. Whether it's a test dataset or reviewing outputs manually, you need a method to compare results.
Start with a Draft: Prompt engineering is an iterative process; don't expect a perfect prompt from the get-go. Just write something decent, then refine it over time.
Prompt Generator: (Personally, I find this tool quite useful.) If you're stuck on how to begin, you can use it to generate a template. The fact that an AI helps you write prompts shows how important prompt engineering is.

II. When to Use Prompt Engineering (and When Not To)

Not all problems can be solved with prompt engineering.

What Prompts Can Solve: Primarily, prompt engineering tackles issues related to output format, tone, factual accuracy, and following instructions. These are things you can influence by changing the input text.
What Prompts Can't Solve: If the AI is slow or too expensive to run, that's not a prompt problem. In such cases, you should consider using a different model or optimizing your code's logic. Don't waste time on prompts for these issues.

III. Core Prompting Techniques for Claude (Key Takeaways!)

This section is crucial; it's where the real magic happens to make Claude perform powerfully. I've highlighted the most important techniques and explained them based on my own experience.

1. Be Clear, Direct, and Detailed

This sounds basic, but it's often overlooked and can lead to significant improvements. Treat Claude like a very smart but clueless new colleague.

Core Idea: If you write a prompt and someone completely unfamiliar with the task can't understand it, Claude will probably mess it up too.
How to Do It:
- Provide Context: Tell Claude why you're doing this task, who the audience is, and what the final goal is. The more context, the better the AI understands.
- State Specific Requirements: For example, if you only want code, explicitly say: "Only output code, no explanations." This saves a lot of cleanup later.
- Use Step-by-Step Instructions: For multi-step tasks, use numbered or bulleted lists. I've found this reduces errors because the AI follows each step.

2. Let Claude "Think" (Chain of Thought)

This technique is especially useful for problems requiring AI reasoning, like math or logic.

Why It's Effective: Like humans solving complex problems, thinking step-by-step reduces errors.
When to Use It: Any task that requires deep thinking, such as complex calculations, data analysis, or writing intricate reports, can benefit. Avoid it for simple tasks, as it increases output length.
How to Guide Its Thinking:
- The simplest way is to add "Think step-by-step" at the end of your prompt.
- For more guidance, outline the steps: "First... Then... Finally..."
- Best Practice (Highly Recommended by me): Use XML tags to clearly separate the thinking process from the final answer, e.g., <thinking> for the thought process and <answer> for the result. This lets you see how it reasoned and easily extract the final output. Remember, if it doesn't output its thought process, assume it didn't really think!

3. Structure with XML Tags

XML tags act like markers within your prompt, helping the AI understand what each part is.

Why Use Them: They prevent the AI from mixing up your instructions, examples, or background information.
How to Use Them:
- Use descriptive tag names, like <instructions> for commands or <document> for content.
- Explicitly refer to the tag names in your instructions, e.g., "Analyze the content within the <contract_text> tag."
- You can nest tags for hierarchical content, like <documents><document>...</document></document>.
- They work even better with other techniques: Combine them with Chain of Thought (using <thinking> and <answer>) or few-shot prompting (using <example> for examples).
My Personal Experience: I even ask the AI to use XML tags in its output, like wrapping JSON data in <json> or code blocks in <code_block>. This makes programmatic parsing incredibly easy later on.

4. Give Claude a "Persona" (System Prompts)

This is a powerful technique that turns Claude into your desired "expert."

How It Works: When interacting with the Claude API, use the system parameter to set the AI's role. For instance, system: "You are an experienced data scientist at a Fortune 500 company."
Impact:
- Significantly improves performance in specialized domains.
- Allows the AI's tone and style to match your requirements.
- Helps the AI stay focused on the task.
My Understanding: The system message acts like installing a "professional plugin" for the AI, giving it the knowledge and mindset of that profession. Your user messages, then, are the specific work tasks. The persistent "persona" goes in system, while specific "tasks" go in user – this is key.

5. Prefill Claude's Response

This trick is like giving Claude a starting point and having it fill in the rest. It's particularly useful when you need a fixed output format.

How It Works: You provide the initial text for the AI's response in the Assistant message. Claude will then continue generating from where you left off. Important: The prefill content cannot end with a trailing space, or it will cause an error.
When to Use It:
- Force Specific Formats: If you want Claude to output JSON directly, you can prefill with { or {"key":. It will then complete the JSON. This is my go-to for consistently generating structured JSON outputs.
- Skip Intros: If you find Claude's introductory phrases verbose, prefilling can make it jump straight to the main content.
- Maintain Persona: In role-playing scenarios, you can prefill with a character's prefix, like [Doctor], to help Claude stay in character.

6. Break Down Complex Tasks (Prompt Chaining)

This technique aligns perfectly with the "divide and conquer" principle in programming. You break a large task into smaller, manageable sub-tasks, with the output of one prompt feeding into the next.

Why Break Them Down: Large tasks are prone to errors. Breaking them down allows the AI to focus on each smaller piece, reducing mistakes.
When to Use It: For multi-step tasks, or those requiring several conversions, such as research, outlining, drafting, and editing.
How to Chain:
- Decompose the entire complex task into sequential sub-tasks.
- Use XML tags to wrap the output of one prompt as the input for the next, ensuring clear data flow.
- Each step should have a single, clear objective.
Advanced Play: Self-Correction. You can have the AI complete a task, then ask it to review its own work for flaws, and finally revise based on that feedback. AI reviewing and correcting itself is pretty powerful.

7. Tips for Long Context Prompting

Claude is powerful with long contexts, but you need to use the right methods to leverage its full capability.

Place Long Data at the Top: If your input includes very long documents (say, over 20,000 tokens), put these documents at the very beginning of your prompt, before your questions and instructions. I've found this helps the AI "see" and understand them better.
Structure Documents with XML Tags: When including multiple documents, wrap each one in a <document> tag and add <source> tags for clarity. This helps the AI differentiate them.
Anchor Responses with Quotes: For long documents, instruct Claude to first extract relevant sections and wrap them in specific tags (e.g., <quotes>). This forces the AI to "retrieve" key information before generating its final answer, which can significantly improve accuracy and reduce hallucinations.

IV. My Closing Thoughts

Prompt engineering isn't something you master just by reading; you have to get your hands dirty and experiment. This guide also points to interactive tutorials, which are great for practice.

Also, AI evolves so fast. What's best practice today might be outdated tomorrow. So, we need to keep learning and keep experimenting.

Ultimately, mastering prompt engineering means learning how to effectively collaborate with AI.