Retrieval Augmented Generation (RAG) with LangChain
Intermediate 3 hours English Course Certificate
Our recommendation
A DataCamp course teaching the technique that lets LLMs answer from your own data instead of just their training set. Across about three hours you progress from foundational RAG through more advanced methods, using LangChain. Hands-on and highly relevant to real-world AI applications, for Python developers who know LLM basics. API usage is billed separately.
Good for: Building retrieval-augmented generation (RAG) systems with LangChain.
Less suitable if: You cannot code Python or want a conceptual-only course.
Requirements: Python and LLM basics; an API key (usage billed separately).
Realistic time: Around 3 hours.
About this course
Retrieval Augmented Generation (RAG) with LangChain teaches the technique that lets LLMs answer from your own data instead of just their training set. Across about three hours you progress from foundational RAG through advanced methods — semantic splitting and graph-based retrieval with Neo4j — integrating external knowledge into model responses.
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
What it costs
DataCamp runs on a subscription — roughly $14/month billed annually (more month-to-month), with the first chapter of each course free to try. A certificate of completion is included with the subscription. The model and embedding APIs used are billed by usage by the provider, separate from DataCamp.
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Retrieval Augmented Generation (RAG) with LangChain