

A Seven Part Prompting Framework for LLM Success
Effective prompting transforms how language models understand and respond to queries. These seven principles create reliable, accurate, and ethical AI interactions.
𝗗𝗮𝘁𝗮 𝗡𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀
Instruct on standardizing data units or formats before analysis. Convert all currency values to USD before summarizing ensures consistency and prevents calculation errors.
𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗟𝗮𝗿𝗴𝗲 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀
Direct the model on processing bulk data efficiently with summaries or sampling. Analyze a sample of 10,000 transactions, not all provides faster responses without overwhelming context windows.
𝗟𝗶𝘃𝗲 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻
Guide use of real time or dynamic data when needed. Incorporate current stock prices from the API ensures responses reflect actual market conditions rather than outdated training data.
𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸
Enable the model to request more data if needed. List missing fields and ask for more info creates interactive conversations that gather complete information before generating final outputs.
𝗗𝗮𝘁𝗮 𝗥𝗲𝗳𝗿𝗲𝘀𝗵 𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝗰𝘆
Specify how often data should be updated or refreshed. Base analysis on weekly updated data sets ensures timeliness while managing computational costs.
𝗦𝗼𝘂𝗿𝗰𝗲 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆
Specify trusted data sources and recency. Use only data from the latest 2025 report grounds responses in authoritative, current information and reduces hallucination risks.
𝗕𝗶𝗮𝘀 𝗮𝗻𝗱 𝗘𝘁𝗵𝗶𝗰𝘀
Prompt to acknowledge data biases or limitations. Note if the dataset lacks diversity or may skew results builds transparency and helps users interpret outputs critically.
These fundamentals apply whether you're building customer support agents, analytical tools, or creative assistants. The quality of your prompts directly determines the reliability of your results.
Effective prompting transforms how language models understand and respond to queries. These seven principles create reliable, accurate, and ethical AI interactions.
𝗗𝗮𝘁𝗮 𝗡𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀
Instruct on standardizing data units or formats before analysis. Convert all currency values to USD before summarizing ensures consistency and prevents calculation errors.
𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗟𝗮𝗿𝗴𝗲 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀
Direct the model on processing bulk data efficiently with summaries or sampling. Analyze a sample of 10,000 transactions, not all provides faster responses without overwhelming context windows.
𝗟𝗶𝘃𝗲 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻
Guide use of real time or dynamic data when needed. Incorporate current stock prices from the API ensures responses reflect actual market conditions rather than outdated training data.
𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸
Enable the model to request more data if needed. List missing fields and ask for more info creates interactive conversations that gather complete information before generating final outputs.
𝗗𝗮𝘁𝗮 𝗥𝗲𝗳𝗿𝗲𝘀𝗵 𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝗰𝘆
Specify how often data should be updated or refreshed. Base analysis on weekly updated data sets ensures timeliness while managing computational costs.
𝗦𝗼𝘂𝗿𝗰𝗲 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆
Specify trusted data sources and recency. Use only data from the latest 2025 report grounds responses in authoritative, current information and reduces hallucination risks.
𝗕𝗶𝗮𝘀 𝗮𝗻𝗱 𝗘𝘁𝗵𝗶𝗰𝘀
Prompt to acknowledge data biases or limitations. Note if the dataset lacks diversity or may skew results builds transparency and helps users interpret outputs critically.
These fundamentals apply whether you're building customer support agents, analytical tools, or creative assistants. The quality of your prompts directly determines the reliability of your results.