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Prompt vs Context vs RAG. I've explained it in a simple way below. 1. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Prompt Engineering is about 𝗰𝗹𝗲𝗮𝗿 𝗶𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀, not magic words. • 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗳𝗶𝗻𝗮𝗹 𝗴𝗼𝗮𝗹: What exactly do you want the AI to produce? • 𝗔𝘀𝘀𝗶𝗴𝗻 𝗮 𝗰𝗹𝗲𝗮𝗿 𝗿𝗼𝗹𝗲: Example: “Act as a legal expert”. • 𝗚𝗶𝘃𝗲 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Show the AI what a good answer looks like • 𝗨𝘀𝗲 𝗳𝗲𝘄-𝘀𝗵𝗼𝘁 𝘀𝗮𝗺𝗽𝗹𝗲𝘀: Provide multiple examples so the pattern is clear • 𝗦𝗲𝘁 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀: Limit length, tone, or structure • 𝗛𝗮𝗻𝗱𝗹𝗲 𝗲𝗱𝗴𝗲 𝗰𝗮𝘀𝗲𝘀: Tell the AI what to do if data is missing or unclear • 𝗗𝗲𝗳𝗶𝗻𝗲 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗳𝗼𝗿𝗺𝗮𝘁: JSON, table, bullets, etc. • 𝗔𝗽𝗽𝗹𝘆 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀: Explicitly state what the AI should NOT do • 𝗦𝘁𝗮𝘁𝗲 𝗮𝘀𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻𝘀: Clarify what the AI can assume • 𝗧𝗲𝘀𝘁 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗽𝗿𝗼𝗺𝗽𝘁𝘀: Try variations to get the best output _____________ 2. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Context Engineering is about 𝗳𝗲𝗲𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗯𝗮𝗰𝗸𝗴𝗿𝗼𝘂𝗻𝗱 𝗱𝗮𝘁𝗮, not everything. • 𝗗𝗲𝗳𝗶𝗻𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗻𝗲𝗲𝗱𝘀: Decide what information is required to answer correctly • 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗱𝗮𝘁𝗮 𝘀𝗼𝘂𝗿𝗰𝗲𝘀: Docs, notes, chat history, APIs, user data • 𝗦𝗲𝗹𝗲𝗰𝘁 𝘂𝘀𝗲𝗳𝘂𝗹 𝘀𝗶𝗴𝗻𝗮𝗹𝘀: Keep only what helps the task • 𝗥𝗲𝗳𝗶𝗻𝗲 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲: Remove irrelevant information • 𝗞𝗲𝗲𝗽 𝗸𝗲𝘆 𝗽𝗼𝗶𝗻𝘁𝘀: Focus on essential facts • 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗲 𝗽𝗮𝘆𝗹𝗼𝗮𝗱: Structure context clearly • 𝗦𝗵𝗿𝗶𝗻𝗸 𝗰𝗼𝗻𝘁𝗲𝘅𝘁: Compress data to fit model limits • 𝗦𝘆𝘀𝘁𝗲𝗺-𝗹𝗲𝘃𝗲𝗹 𝗰𝗼𝗻𝘁𝗲𝘅𝘁: Global rules like tone, behavior, style • 𝗧𝗮𝘀𝗸-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗰𝗼𝗻𝘁𝗲𝘅𝘁: Information needed only for this task • 𝗧𝗿𝗮𝗰𝗸 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲: Maintain memory across conversations _____________ 3. 𝗥𝗔𝗚 RAG is used to bring 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 into AI responses. • 𝗟𝗼𝗮𝗱 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝘀: PDFs, websites, internal docs, databases • 𝗦𝗽𝗹𝗶𝘁 𝗶𝗻𝘁𝗼 𝗰𝗵𝘂𝗻𝗸𝘀: Break large text into small pieces • 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀: Convert chunks into vectors (meaning-based numbers) • 𝗦𝗮𝘃𝗲 𝘃𝗲𝗰𝘁𝗼𝗿𝘀: Store them in a vector database • 𝗕𝘂𝗶𝗹𝗱 𝘀𝗲𝗮𝗿𝗰𝗵 𝗶𝗻𝗱𝗲𝘅𝗲𝘀: Make data searchable by meaning • 𝗥𝗲𝗰𝗲𝗶𝘃𝗲 𝘂𝘀𝗲𝗿 𝗾𝘂𝗲𝗿𝘆: User asks a question • 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗲 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗽𝗮𝘀𝘀𝗮𝗴𝗲𝘀: Find the closest matching chunks • 𝗔𝗽𝗽𝗹𝘆 𝗺𝗲𝘁𝗮𝗱𝗮𝘁𝗮 𝗳𝗶𝗹𝘁𝗲𝗿𝘀: Narrow results to relevant data • 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝗮𝗻𝘀𝘄𝗲𝗿: LLM uses retrieved context to respond • 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: Measure relevance and usefulness 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀? <> 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 controls how you ask <> 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 controls what the AI knows <> 𝗥𝗔𝗚 controls where the knowledge comes from ✅ Repost for others so they can understand these key differences.