Back

Rahul Agarwal

Founder | Agentic AI... • 1d

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.

Reply
2
13

More like this

Recommendations from Medial

Image Description

Account Deleted

Hey I am on Medial • 4m

⚡️ GPT-4.1 just dropped in ChatGPT This model is a beast: ⚡️ Best-in-class for coding — spots bugs, writes full web apps, no sweat. ⚡️ Handles complex instructions — throw in your massive, multi-step prompt. It’ll get it. ⚡️ Insane context window —

See More
1 Reply
9

Inactive

AprameyaAI • 1y

Meta has released Llama 3.1, the first frontier-level open source AI model, with features such as expanded context length to 128K, support for eight languages, and the introduction of Llama 3.1 405B. The model offers flexibility and control, enabli

See More
Reply
2
9
Image Description

Account Deleted

Hey I am on Medial • 3m

20 tips for coding by prompt — and no, these aren’t just gimmicks. Most devs don’t realize how far prompt-based coding has come. You’re not just asking for syntax help anymore. You’re running full workflows, automating chores, and shipping faster —

See More
1 Reply
6
15
Image Description
Image Description

Rahul Agarwal

Founder | Agentic AI... • 15d

Well, Lovable is great for building apps. But how does Lovable actually produce full apps? I'll break down the entire process of how lovable works step by step. 1. 𝗨𝘀𝗲𝗿 𝗜𝗻𝗽𝘂𝘁 (𝗣𝗿𝗼𝗺𝗽𝘁 𝗦𝘁𝗮𝗴𝗲) • You type your idea in Lovable (e.g.

See More
2 Replies
3
13

mg

mysterious guy • 4m

30 AI Buzzwords Explained for Entrepreneurs 1) Large Language Model (LLM) LLMs are like super-smart computer programs that can understand and do almost anything you ask them using regular language. Think of tools like ChatGPT or Gemini – they're a

See More
Reply
8

Rifayu Deen

From code to company... • 2m

🔍 𝟯 𝗞𝗲𝘆 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 𝗧𝗵𝗮𝘁 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗟𝗟𝗠 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 & 𝗥𝗲𝗱𝘂𝗰𝗲𝗱 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗢𝘂𝗿 𝗔𝗜 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 While solo-building Zinkmail — my AI assistant that cleans up inboxes and surfaces w

See More
Reply
1
4
Image Description
Image Description

Edgar Manalo

Hey I am on Medial • 1m

The #1 Platform to Chat & Compare AI Models Let’s be real every AI model has strengths and blind spots. Claude is great with context. ChatGPT-4 nails structure + formatting. Gemini feels more fluid in conversation. Perplexity? Sources everything but

See More
6 Replies
2
9
1
Image Description
Image Description

Rahul Agarwal

Founder | Agentic AI... • 1m

Simple explanation of Traditional RAG vs Agentic RAG vs MCP. 1. 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) • 𝗦𝘁𝗲𝗽 1: 𝗨𝘀𝗲𝗿 𝗮𝘀𝗸𝘀 𝗮 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻. Example: “𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘤𝘢𝘱𝘪�

See More
3 Replies
34
41
4
Image Description
Image Description

Shuvodip Ray

 • 

YouTube • 1y

Medium.com was created by Ev Williams, one of the founders of Twitter and Blogger, in August 2012. It is considered a natural extension of Williams' previous companies, as it aims to provide a platform for people to share their ideas and stories. The

See More
4 Replies
9
Image Description

mg

mysterious guy • 4m

᠅ Founder Tip: Copying a startup model is easy—copying their context is impossible Founders often say: “It worked in the US. Let’s do the same in India.” But what worked there may not work here. The model is visible. The why behind it isn’t. Here’s

See More
1 Reply
1
9

Download the medial app to read full posts, comements and news.