Build Production-Ready Agentic-RAG Applications From Scratch
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Learning format
Subcategory
Price
Price may change · updated within 1–2 weeks
Course language
What You'll Learn
- Applying software engineering best practices to AI apps
- Designing effective prompts for controlled generation
- Mitigating hallucinations through structured output checks
- Creating chatbot interfaces with citations in React
- Building scalable async applications with FastAPI
- Orchestrating agentic RAG pipelines with LangGraph
Best For
- Those wanting production-ready, not prototype, code
- Founders proving AI product value
- Full-stack engineers adding RAG quickly
- ML/LLM engineers shipping agentic RAG
Not For
- Those wanting a large, mass-market community
- People wanting a fully self-paced format
- Those wanting purely theoretical AI content
- Complete beginners to Python or JavaScript
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About the creator
Damien Benveniste
View full profileTechnology & AI Engineering · Long Beach, California
Damien Benveniste holds a PhD in theoretical Physics and spent over a decade in Machine Learning, most recently as a Machine Learning Tech Lead at Meta working on ad ranking model optimization at scale. Based in Long Beach, California, he founded The AiEdge Newsletter (82K+ subscribers) and now teaches production-ready AI engineering courses on Maven, including agentic RAG application development.
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Reviews, Scores & Student Outcomes
Unclaimed ProfileThis course focuses specifically on production-readiness, not just prototyping, building a full agentic RAG stack (LangGraph, FastAPI, React) that runs locally and deploys via a fork-and-ship monorepo. Across 6 live sessions, students build an ingestion pipeline, hardened agentic workflows with retries and safety checks, a chat UI with citations, and deployment scripts, walking away with a real, working application rather than just a notebook demo, plus a framework choice memo for future projects.
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