Prompt Engineering: 10 Practical Hacks for Better AI Results

Why Prompt Engineering at All?

Prompt Engineering is faster, cheaper, and more flexible than Fine-tuning – especially if you want to experiment quickly and see results immediately. It saves GPU resources, avoids expensive retrains after model updates, and retains the model's general knowledge – while achieving comparable quality improvement. Anthropic has released an official Prompt-Engineering documentation and we have summarized the key findings here.

1. Write Clearly, Directly & in Detail

• Provide context (purpose, target audience, success criteria)

• Use explicit step-by-step instructions

• Internal “colleague test”: If a newcomer understands your prompt, so will Claude.

2. Multishot Prompting: Learning by Example

3-5 relevant and diverse examples in <example> tags significantly increase accuracy, consistency, and format fidelity. Essential for structured outputs.

3. Chain-of-Thought (CoT): “Think Aloud”

Add sentences like “Think step by step” or encapsulate the thinking in <thinking> tags to solve complex tasks more transparently and with fewer errors. Pay attention to latency versus added value.

4. XML Tags for Structure

Clearly separate Context, Instructions, and Examples with tags like <instructions>, <data>, or <formatting>. This prevents mixing, facilitates parsing, and can be perfectly combined with CoT or Multishot.

5. System Prompts: Assigning a Role

Set the system parameter to put Claude in a role (e.g., “You are the CFO of a SaaS unicorn”). This enhances subject accuracy, tone, and focus in complex use cases.

6. Prefill: Sketching the Answer in Advance

By pre-filling the first Assistant message you can

• skip the boring "As an AI...",

• enforce a fixed JSON structure, or

• keep character role-plays stable.
  Only available in non-extended-thinking modes.

7. Prompt Chaining: Staging Subtasks

Break down mammoth tasks into logical mini-prompts (Research → Synthesis → Review). Each stage receives maximum attention, sources of errors are easier to isolate, and you can parallelize intermediate results.

8. Self-Correction Loops

Let Claude assess his own work (A–F scale, checklist, etc.) and revise it in a second step. Saves manual QA effort – especially with sensitive content.

9. Long-Context Tips (200k Tokens)

• Long documents first, query at the end → up to 30% better answers.

• Encapsulate documents & metadata in <document> tags.

• Quote Grounding: Request relevant quotes before the analysis begins to filter out "noise".

10. Quick-Check List

Conclusion

With these ten techniques, you can transform Claude from a general assistant into a precise specialist for your business challenges. Start with clear instructions, add examples, and gradually increase the complexity – then you will see how the quality of your AI results measurably improves.

Pro Tip: Document every prompt change along with metrics. This way, you build an internal prompt library that makes your team more efficient in the long run.