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Rahul Agarwal
Founder | Agentic AI...ย โขย 2m
Fine-tune vs Prompt vs Context Engineering. Simple step-by-step breakdown for each approach. ๐๐ถ๐ป๐ฒ-๐ง๐๐ป๐ถ๐ป๐ด (๐ ๐ผ๐ฑ๐ฒ๐น-๐๐ฒ๐๐ฒ๐น ๐๐๐๐๐ผ๐บ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป) ๐๐น๐ผ๐: 1. Collect Data โ Gather domain-specific info (e.g., legal docs). 2. Start with Base Model โ Use an existing large AI. 3. Train with Examples โ Feed dataset with correct answers. 4. Adjust Model Settings โ Update internal โmemory.โ 5. Store New Knowledge โ Learning stays permanently. 6. Test Results โ Check accuracy. 7. Update Training if Needed โ Add more data if required. 8. Deploy Fine-Tuned Model โ Ready for real-world use. ๐ Best when you need the model to ๐ฑ๐ฒ๐ฒ๐ฝ๐น๐ ๐๐ป๐ฑ๐ฒ๐ฟ๐๐๐ฎ๐ป๐ฑ ๐ฎ ๐ณ๐ถ๐ฒ๐น๐ฑ. __________________________________________ ๐ฃ๐ฟ๐ผ๐บ๐ฝ๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด (๐๐ป๐ฝ๐๐-๐๐ฒ๐๐ฒ๐น ๐ข๐ฝ๐๐ถ๐บ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป) ๐๐น๐ผ๐: 1. Set Goal โ Define what the model should do. 2. Choose Prompt Style โ Write clear instructions. 3. Provide Examples โ Show sample inputs/outputs. 4. Test & Improve โ Try versions, refine wording. 5. Balance Creativity & Logic โ Keep clear but flexible. 6. Integrate Tools โ Use with supporting software. 7. Gather Feedback โ Learn from users. 8. Ensure Consistency โ Stable, repeatable answers. ๐ Best when you want ๐ฏ๐ฒ๐๐๐ฒ๐ฟ ๐ฎ๐ป๐๐๐ฒ๐ฟ๐ ๐๐ถ๐๐ต๐ผ๐๐ ๐ฟ๐ฒ๐๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด. ___________________________________________ ๐๐ผ๐ป๐๐ฒ๐ ๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด (๐ฅ๐๐ป๐๐ถ๐บ๐ฒ ๐๐ผ๐ป๐๐ฟ๐ผ๐น) ๐๐น๐ผ๐: 1. Set Context Scope โ Decide needed info. 2. Chunk Data โ Break into small pieces & embed. 3. Store in Vector DB โ Make searchable. 4. Retrieve Relevant Chunks โ Fetch only whatโs useful. 5. Query by User Input โ Match based on question. 6. Pick Closest Matches โ Get high-similarity results. 7. Build Context โ Assemble chunks. 8. Insert into Prompt โ Add to model input. 9. Stay Within Token Limit โ Avoid overload. 10. Keep Order & Format โ Ensure clarity. 11. Update Context โ Adjust as conversation grows. ๐ Best when you want AI to ๐ฎ๐ฐ๐ฐ๐ฒ๐๐ ๐น๐ฎ๐ฟ๐ด๐ฒ ๐ฑ๐ฎ๐๐ฎ ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ ๐น๐ถ๐๐ฒ to give accurate and context-aware responses. โ ๐๐ป ๐๐ต๐ผ๐ฟ๐: โข ๐๐ถ๐ป๐ฒ-๐ง๐๐ป๐ถ๐ป๐ด โ Changes the model itself (permanent learning). โข ๐ฃ๐ฟ๐ผ๐บ๐ฝ๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด โ Changes ๐ฉ๐ฐ๐ธ ๐บ๐ฐ๐ถ ๐ข๐ด๐ฌ (better instructions). โข ๐๐ผ๐ป๐๐ฒ๐ ๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด โ Changes ๐ธ๐ฉ๐ข๐ต ๐ช๐ฏ๐ง๐ฐ ๐ต๐ฉ๐ฆ ๐ฎ๐ฐ๐ฅ๐ฆ๐ญ ๐ด๐ฆ๐ฆ๐ด (runtime memory). โ Repost for others in your network to help them understand.
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