PyBerry Tech 🐍🍓
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@PyBerryTech
🐍 Python | 🤖 AI | ☁️ Cloud Sharing practical projects, tips & real learning for devs. Learn & grow together. 📩 [email protected]
RAG isn’t a feature. It’s an architecture. Most people think it’s: “Add docs → retrieve → generate” That works… for demos. But in production? That’s where it breaks. Real-world RAG looks like this: • Re-ranking → filters better context • Hybrid search → semantic + keyword = higher accuracy • Multimodal → text, PDFs, images, audio • Graph RAG → relationships > raw chunks • Agentic RAG → AI decides *how* to retrieve • Multi-agent → retrieve, verify, summarize separately The shift is simple: ❌ Basic RAG → fetch & hope ✅ Advanced RAG → retrieve, refine, reason Because at the end of the day: Better context = better answers. So the real question is: Are you still building demo RAG… or production-grade systems? 👀 #AI #RAG #LLM #GenAI #AIArchitecture
A cheat sheet to data structures, v/@PythonPr.
STOP GIVING VAGUE PROMPTS TO LLM. Bad prompts = Bad results. Use these 12 prompting techniques instead & see the magic: 1 Zero-Shot Prompting 2 Few-Shot Prompting 3 Role Prompting 4 Instruction Prompting 5 Format Prompting 6 Retrieval-Augmented Generation (RAG) 7 Prompt Chaining 8 Reflection Prompting 9 Chain-of-Thought (CoT) 10 Self-Consistency 11 Tree of Thoughts (ToT) 12 Meta Prompting What else should make this list? —— 💾 Save this for later & RT to help others learn AI prompting. 👤 Follow @systemdesignone + turn on notifications.
