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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

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 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

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 ↓

Most people don't even know these basics of LLM's. I've explained it in a simple way below. 1. 𝗗𝗮𝘁𝗮 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 LLMs are trained on massive amounts of text from books, websites, articles, and documents so they can learn how language is

Here's a short roadmap for learning CSS: 1. Basic CSS: Start with understanding selectors, properties, and values to style HTML elements. 2. Box Model: Learn how the box model works, including margin, padding, border, and content. 3. Layout: Dive

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