RAG for Professionals with LangGraph, Python and OpenAI — 91% Off Coupon

Build production-ready AI Systems for internal Business Documents using LangChain, LangGraph, OpenAI, Chroma & Python

4.8 249 students enrolledCreated by Alexander HagmannLast updated: 🌐 English

Course Overview — Key Details

A quick-reference summary of the most important course details: provider, instructor, difficulty, duration, and what the coupon covers.

•Course Title: RAG for Professionals with LangGraph, Python and OpenAI
•Provider: Udemy (listed via CourseSpeak)
•Instructor: Alexander Hagmann
•Coupon Verified On: January 4, 2026
•Difficulty Level: Advanced
•Category: Business
•Subcategory: Retrieval Augmented Generation (RAG)
•Duration: 11h of on-demand video
•Language: English
•Access: Lifetime Access · Mobile & TV compatible
•Certificate: Certificate of completion included
•Top Learning Outcomes: Explain what RAG is, why it’s needed, and when it outperforms plain LLMs · Design your own Enterprise RAG Solutions for internal Documents & Knowledge bases · Use LangChain to build Chatbots, Summarization Pipelines and RAG chains
•Prerequisites: Comfortable with basic Python Programming · Ability to install software (Anaconda, Python packages) on your machine
•Coupon: Click REDEEM COUPON below to apply discount
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What You'll Learn

Skills and competencies you'll gain from this Udemy course:

✓Explain what RAG is, why it’s needed, and when it outperforms plain LLMs .
✓Design your own Enterprise RAG Solutions for internal Documents & Knowledge bases .
✓Use LangChain to build Chatbots, Summarization Pipelines and RAG chains .
✓Use LangGraph to design graph-based, agentic AI Workflows .
✓Load, split and chunk Documents of different Types and sizes effectively .
✓Apply different Summarization Strategies (Stuff, Map-Reduce, Refine) .
✓Create Embeddings and use Vector Stores (FAISS, Chroma) for Retrieval .
✓Evaluate and tune Retrieval Strategies (similarity, thresholds, MMR, multi-query) .
✓Manage Vector Stores with Metadata for powerful filtering and search .
✓Build a dynamic, persistent Chroma vector DB from scratch .
✓Implement automated Vector DB updates based on File and Metadata Changes .
✓Swap out LLMs, Embeddings and Vector DBs to meet Privacy & Scalability needs.

Course Requirements & Prerequisites

Background knowledge or tools recommended before starting this course:

•Comfortable with basic Python Programming
•Ability to install software (Anaconda, Python packages) on your machine
•Willingness to spend a few Dollars on API calls (less than 5 USD)
•Stable Internet Connection and ability to Stream HD Videos
•Optional but helpful: prior exposure to ChatGPT / LLMs conceptually

About This Udemy Course

Full course description including curriculum, tools covered, and learning methodology:

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About the Instructor

This course is taught by Alexander Hagmann. For full instructor bio, credentials, and other courses they teach, visit the instructor profile on Udemy.

•Instructor: Alexander Hagmann
•Field: Business
•Teaching Style: Practical, project-based learning (as described in course curriculum)

Is the RAG for Professionals with LangGraph, Python and OpenAI Coupon Worth It?

RAG for Professionals with LangGraph, Python and OpenAI is a business course offered on Udemy by instructor Alexander Hagmann, spanning 11h of on-demand content. It holds a 4.8/5 rating from over 249 enrolled students.

Through CourseSpeak, you can access this course with a 91% discount coupon. The coupon was last verified on January 4, 2026. Udemy coupons are time-limited and claimed on a first-come basis — we recommend redeeming as soon as possible.

New to redeeming coupons? Visit our How to Redeem Udemy Coupon on CourseSpeak for detailed instructions on how to apply coupon codes.

✓ Our Take: Based on the rating (4.8/5) and enrollment numbers (249 students), this course appears well-regarded in its category. Use the coupon to access it at a significantly reduced price — and judge for yourself using Udemy's 30-day money-back guarantee.

Course Rating Summary

Aggregate rating data sourced from Udemy as of January 2026. For individual student reviews, visit the course page directly.

4.8
249 ratings
5 stars
75%
4 stars
15%
3 stars
6%
2 stars
2%
1 star
2%

* Rating distribution is estimated. For exact per-star counts, visit the Udemy course page.

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