@Ryrenz: Guys, I found another gem of a course: Build a Production-Grade RAG System from Scratch in 7 Weeks — 7.7k stars on GitHub, hands-on coding throughout, not a slides-only course. Most RAG tutorials out there jump straight to vector search; the demo works but crashes in production. This course follows the real path used in companies...
Summary
A 7-week course with 7.7k stars on GitHub, building a production-grade RAG system from scratch, covering Docker, FastAPI, hybrid search, LangGraph agentic RAG, and a Telegram bot, with hands-on coding throughout.
View Cached Full Text
Cached at: 07/15/26, 03:58 PM
A Learner-Focused Journey into Production RAG Systems
Learn to build modern AI systems from the ground up through hands-on implementation
Master the most in-demand AI engineering skills: RAG (Retrieval-Augmented Generation)
Complete Week 7 architecture showing Telegram bot integration with the agentic RAG system
Detailed LangGraph workflow showing decision nodes, document grading, and adaptive retrieval
🎉 Ready to Start Your AI Engineering Journey?
Begin with the Week 1 setup notebook and build your first production RAG system!
For learners who want to master modern AI engineering
Built with love by Shirin Khosravi Jam & Shantanu Ladhwe
Similar Articles
@GitHub_Daily: Want to understand how RAG really works? Online tutorials either skip steps or directly call cloud APIs, leaving the intermediate process invisible. RAG from Scratch breaks the entire pipeline into a dozen or so small experiments, each step running with local models—no black box. From text chunking, vectorization, retrieval to final generation, the code is all right there...
Introduces the open-source project RAG from Scratch, which fully breaks down the RAG pipeline through step-by-step local code experiments, covering text chunking, vectorization, retrieval, reranking, query rewriting and other advanced strategies, helping developers understand RAG implementation from the ground up.
@mate_mattt: I built a real, runnable RAG project and a Notebook RAG practical course, breaking down RAG pixel by pixel: Markdown chunking → FTS5 / BM25 → Embedding vector search → Hybrid recall RRF → Cross-Encod…
This is a hands-on project for learning local RAG retrieval core from scratch, including Notebook and real runnable code. It covers the complete workflow: Markdown chunking, BM25, Embedding vector search, hybrid recall RRF, Cross-Encoder re-ranking, and comes with evaluation metrics.
jamwithai/production-agentic-rag-course
A learner-focused hands-on course that teaches building production-grade RAG systems from scratch, covering keyword search, hybrid retrieval, agentic RAG with LangGraph, and Telegram bot integration.
@CycleDecoded: Fellow devs working on AI vector databases and RAG can save six months of detours! This viral GitHub project combines "real-time data + vector retrieval + LLM pipeline" into one — write 30 lines of Python and you've got an enterprise-grade RAG system in seconds, no need to deal with all kinds of complex...
Pathway's open-source llm-app is a framework for building enterprise-grade RAG systems. It supports real-time data sync, built-in vector retrieval, and comes with ready-made cloud templates. It has earned over 59,000 stars on GitHub.
@axichuhai: Hey everyone, I've found another GitHub treasure open-source project — hello-agents has shot straight to the top of the GitHub trending list and is still climbing! It systematically organizes AI and Agent from theory to practice into an open-source curriculum, covering Agentic RL, SFT, …
Discovered an open-source GitHub project hello-agents, which organizes a complete open-source course from theory to practice on AI Agents, covering core skills like Agentic RL, SFT, GRPO, and has reached the top of GitHub trending.