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Use model-specific prompting techniques: Claude likes XML, GPT likes markdown, Gemini handles multimodal well.

Start your prompt by telling the model to act as an expert persona. For example, 'Act as a senior copywriter specializing in luxury brands.' This primes the model to adopt the desired tone, style, and domain knowledge, leading to higher-quality outputs.

Instruct the LLM to adopt a specific persona or role (e.g., 'You are a helpful assistant specializing in physics.'). This can significantly shape the tone, style, and content of its responses.

Similar to text, image generation benefits from iteration. Start with a basic prompt, then refine it by adding details, styles (e.g., 'photorealistic', 'impressionist'), or camera angles.

Incorporate keywords that suggest the desired style or tone, e.g., 'formal', 'casual', 'humorous', 'academic', 'empathetic'.

Structure prompts by defining: WHAT you want the LLM to do, WHY it's important (context), and HOW it should do it (format, style, constraints).