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Rahul Agarwal
Founder | Agentic AI... • 5m
Fine-tune vs Prompt vs Context Engineering. Simple step-by-step breakdown for each approach. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 (𝗠𝗼𝗱𝗲𝗹-𝗟𝗲𝘃𝗲𝗹 𝗖𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻) 𝗙𝗹𝗼𝘄: 1. Collect Data → Gather domain-specific info (e.g., legal docs). 2. Start with Base Model → Use an existing large AI. 3. Train with Examples → Feed dataset with correct answers. 4. Adjust Model Settings → Update internal “memory.” 5. Store New Knowledge → Learning stays permanently. 6. Test Results → Check accuracy. 7. Update Training if Needed → Add more data if required. 8. Deploy Fine-Tuned Model → Ready for real-world use. 👉 Best when you need the model to 𝗱𝗲𝗲𝗽𝗹𝘆 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗮 𝗳𝗶𝗲𝗹𝗱. __________________________________________ 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 (𝗜𝗻𝗽𝘂𝘁-𝗟𝗲𝘃𝗲𝗹 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻) 𝗙𝗹𝗼𝘄: 1. Set Goal → Define what the model should do. 2. Choose Prompt Style → Write clear instructions. 3. Provide Examples → Show sample inputs/outputs. 4. Test & Improve → Try versions, refine wording. 5. Balance Creativity & Logic → Keep clear but flexible. 6. Integrate Tools → Use with supporting software. 7. Gather Feedback → Learn from users. 8. Ensure Consistency → Stable, repeatable answers. 👉 Best when you want 𝗯𝗲𝘁𝘁𝗲𝗿 𝗮𝗻𝘀𝘄𝗲𝗿𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗿𝗲𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴. ___________________________________________ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 (𝗥𝘂𝗻𝘁𝗶𝗺𝗲 𝗖𝗼𝗻𝘁𝗿𝗼𝗹) 𝗙𝗹𝗼𝘄: 1. Set Context Scope → Decide needed info. 2. Chunk Data → Break into small pieces & embed. 3. Store in Vector DB → Make searchable. 4. Retrieve Relevant Chunks → Fetch only what’s useful. 5. Query by User Input → Match based on question. 6. Pick Closest Matches → Get high-similarity results. 7. Build Context → Assemble chunks. 8. Insert into Prompt → Add to model input. 9. Stay Within Token Limit → Avoid overload. 10. Keep Order & Format → Ensure clarity. 11. Update Context → Adjust as conversation grows. 👉 Best when you want AI to 𝗮𝗰𝗰𝗲𝘀𝘀 𝗹𝗮𝗿𝗴𝗲 𝗱𝗮𝘁𝗮 𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗹𝗶𝘃𝗲 to give accurate and context-aware responses. ✅ 𝗜𝗻 𝘀𝗵𝗼𝗿𝘁: • 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 → Changes the model itself (permanent learning). • 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 → Changes 𝘩𝘰𝘸 𝘺𝘰𝘶 𝘢𝘴𝘬 (better instructions). • 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 → Changes 𝘸𝘩𝘢𝘵 𝘪𝘯𝘧𝘰 𝘵𝘩𝘦 𝘮𝘰𝘥𝘦𝘭 𝘴𝘦𝘦𝘴 (runtime memory). ✅ Repost for others in your network to help them understand.
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