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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. Sta

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

𝗧𝗵𝗲 𝟱 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘀𝘁𝘂𝗰𝗸 𝘄𝗶𝘁𝗵 𝗺𝗲 𝗿𝗶𝗴𝗵𝘁 𝗮𝗳𝘁𝗲𝗿 𝗹𝗮𝘂𝗻𝗰𝗵 (𝗻𝗼𝘁 “𝗯𝗲𝘀𝘁,” 𝗷𝘂𝘀𝘁 𝘂𝗻𝗳𝗼𝗿𝗴𝗲𝘁𝘁𝗮𝗯𝗹𝗲): • Claude 3.5 Sonnet – balanced, smooth, felt almost human. • o3 – search + s

Most people building modern AI systems miss these steps. I've explained each step in a simple way below. 1. 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗜𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆 How multiple AI agents work together as a system. Step-by-step: • 𝗨𝘀𝗲𝗿 𝗥𝗲𝗾𝘂�

Most people don't even know these basics of RAG. I've explained it in a simple way below. 1. 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 Convert documents into a format that AI can quickly search later. Step-by-step: • 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁: You start with files like PDFs, Word

Most of us rely on prompting to get the work done through ChatGPT. Even I used to follow this technique until one of my friends taught me how to master Context Engineering. Here's how it works: Prompting vs Context Engineering Prompting is about a

2 ways AI systems today generate smarter answers. I’ve explained both in simple steps below. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) (𝘴𝘵𝘦𝘱-𝘣𝘺-𝘴𝘵𝘦𝘱) RAG lets AI fetch and use real-time external information to ge

Hands down the simplest explanation of AI agents using LLMs, memory, and tools. A user sends an input → the system (agent) builds a prompt and may call tools and memory-search (RAG) → agent decides and builds an answer → the answer is returned to th

Everyone has access to the same AI now. So why do some teams still outperform others? The difference is not the model. It is the context around it. Two teams can use the same GPT, Claude, or Gemini and get completely different outcomes. One ends up

Same Performance as Claude Sonnet 4 next to Kimi K2 Qwen3-Coder is simply one of the best coding model we've ever seen. → Still 100% open source → Up to 1M context window 🔥 → 35B active parameters They're releasing CLI tool as well ↓