DADataCamp
Retrieval Augmented Generation (RAG) with LangChain
Intermediate 3 hours English Completion Certificate
What you'll learn
Explain how RAG grounds LLMs
Split and embed external data
Store and query a vector index
Apply semantic splitting
Build graph-based RAG with Neo4j
Integrate retrieval into LLM responses
This course includes
3h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
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Prices & availability can change — confirm on the provider's site. We're not affiliated with any single provider.
Instructor
MN
Meri Nova
DataCamp instructor
— learners— courses — instructor rating
Created by Meri Nova, a machine learning engineer, with DataCamp's James Chapman — combining applied ML and curriculum design.
Requirements
- Python; LangChain basics recommended
Who this course is for
- Developers building knowledge-grounded AI
- AI engineers using LangChain
- Data scientists extending LLMs
About this provider
DA
DataCamp
Data science and analytics learning platform. 10M+ learners, hands-on coding exercises.
4.4 trust score
Frequently asked questions
Retrieval Augmented Generation grounds an LLM's answers in external data you provide, improving accuracy and keeping responses up to date.
Yes — it goes beyond vector RAG into graph-based retrieval using Neo4j, alongside techniques like semantic splitting.
It's intermediate and Python-based; some LangChain familiarity helps. Start with the LangChain applications course if it's all new.
Yes — DataCamp issues a shareable statement of completion.
About three hours.