create-prompt

To write effective prompts for Claude and GPT, define clear objectives, structure content using XML or markdown, incorporate few-shot examples, and apply advanced techniques like chain-of-thought reasoning to guide the AI's output accurately.

Chain-of-thought reasoning in prompt engineering is an advanced technique that directs the LLM to break down complex problems into intermediate logical steps, significantly improving the accuracy and reliability of the final output.

You should use XML tags to structure prompts for LLMs when you need to clearly separate context, instructions, constraints, and desired output formats, ensuring the AI processes distinct sections without conflating the information.

To create a prompt for summarizing technical documents, define the specific context, outline the core task, set constraints for a non-technical audience, and specify the desired output format to ensure a concise and accurate summary.

Few-shot examples optimize AI prompts by providing the model with specific input-output pairs within the query, demonstrating the exact pattern, tone, and structure the LLM should follow when generating its own response.

Prompt engineering applies shared best practices like clarity, structure, and examples for both Claude and GPT models, utilizing advanced techniques from Anthropic and OpenAI research to ensure optimal performance across different LLMs.