PyBerry Tech ๐๐
1.6K posts
@PyBerryTech
๐ Python | ๐ค AI | โ๏ธ Cloud Sharing practical projects, tips & real learning for devs. Learn & grow together. ๐ฉ [email protected]
RAG isnโt a feature. Itโs an architecture. Most people think itโs: โAdd docs โ retrieve โ generateโ That worksโฆ for demos. But in production? Thatโs where it breaks. Real-world RAG looks like this: โข Re-ranking โ filters better context โข Hybrid search โ semantic + keyword = higher accuracy โข Multimodal โ text, PDFs, images, audio โข Graph RAG โ relationships > raw chunks โข Agentic RAG โ AI decides *how* to retrieve โข Multi-agent โ retrieve, verify, summarize separately The shift is simple: โ Basic RAG โ fetch & hope โ Advanced RAG โ retrieve, refine, reason Because at the end of the day: Better context = better answers. So the real question is: Are you still building demo RAGโฆ or production-grade systems? ๐ #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? โโ ๐พ Save this for later & RT to help others learn AI prompting. ๐ค Follow @systemdesignone + turn on notifications.
