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In a production environment, A/B test different prompt variations to empirically determine which ones yield the best results for your key metrics.

Ask the LLM to review its own previous output for errors or areas of improvement. 'Review your previous response. Are there any inaccuracies or ways to make it clearer?'

If your prompt uses domain-specific jargon or acronyms the LLM might not know, provide a small glossary or definitions within the prompt.

For complex tasks, chain multiple prompts together. The output of one LLM call becomes the input (or part of the input) for the next. This allows for sophisticated workflows.

The quality of your LLM's output is highly dependent on the quality of your input prompt. Clear, well-structured, and relevant prompts lead to better results.

For recurring tasks, create prompt templates with placeholders for variable inputs. This ensures consistency and makes it easier to automate prompt generation.

Start your prompt with 'Assume the role of a [expert/character].' This helps the LLM adopt the desired persona and knowledge base more effectively.

Clearly list all constraints the output must satisfy. For example, 'Write a poem about a cat that is exactly 10 lines long and mentions the moon.'

A more advanced technique where the LLM is prompted to generate both reasoning traces and actions to take to solve a problem, often interacting with external tools.

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).

Example: 'I am a software developer. Pretend to be a senior architect and review my proposed design [design details].' Helps frame interaction.

For complex tasks, reveal information progressively. Start with basic requirements, get initial output, then add more specific constraints or details.

Ask the LLM to think about its thinking process: 'Before answering, consider what approach would be most effective for this problem and explain your reasoning strategy.'

When dealing with sensitive topics, instruct the LLM to consider emotional context: 'Respond with empathy and understanding, considering the emotional state of someone facing this situation.'

Use prompts like 'What would happen if...' or 'How would the outcome change if...' to explore alternative scenarios and their implications.

Ask the LLM to consider multiple perspectives: 'Analyze this issue from the viewpoint of [stakeholder A], [stakeholder B], and [stakeholder C].'

Use Socratic method prompts: 'Instead of giving me the answer, ask me questions that will help me discover the solution myself.'

If initial constraints are too restrictive and yield poor results, gradually relax them: 'If the previous constraints are too limiting, suggest the closest possible alternative.'

Establish a protocol: 'After each response, I'll provide feedback. Use this feedback to improve your next response while maintaining the core requirements.'

Define what you DON'T want as clearly as what you DO want. Example: 'Write a professional email. Do not use slang, emojis, or overly casual language.'