signalGitHub Trending2026-10-05
jamwithai/production-agentic-rag-course
This learner-focused course guides you through building a production-grade RAG system, specifically an arXiv Paper Curator that fetches and parses academic papers. It follows the professional path of mastering keyword search with BM25, then enhancing with vector semantic retrieval for hybrid search. By week 7, you integrate agentic RAG with LangGraph and a Telegram bot, with monitoring via Langfuse and caching via Redis.
- for who
- Developers and AI engineers learning to build production RAG systems.
- why now
- This week's new release adds agentic RAG with LangGraph and Telegram.
- what changes
- Learners shift from AI-first shortcuts to a search-first foundation, building RAG systems that mirror industry practice.
- to do
- Start the course by cloning the repository and following the 7-week plan to build the arXiv Paper Curator.
key points
- 7-week course builds arXiv Paper Curator with production RAG stack
- Employs BM25 keyword search, then vector hybrid retrieval for recall
- Agentic RAG with LangGraph and Telegram bot for mobile access
#rag#agent workflow#github open source project#production systems#learning path
score
score 8 out of 10. 0-10: how dense the facts are, multiplied by how much you can do with them after reading. 8+ means the topic's evidence bar is met: benchmarks and availability for a new model, amount and investors for a funding round, revenue figures for a solo-money story. Below 5 an item does not enter the digest. A press release scores 3 or less, a reprint loses 2, anything older than 14 days loses 1, a headline that misleads loses 3.
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