Learn all features of LangChain & build Generative AI applications with Memory, RAG, Tools, Agents etc. using LangChain
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Instruction covers essential LangChain components such as LCEL runnables, various memory types (Buffer, Window, Summary), and full RAG workflows including chunking and embedding.
The curriculum extends beyond the core framework to include application monitoring via LangSmith and interface creation using Streamlit.
Editorial course preview
These 2 complementary views highlight concrete, legible examples from the course presentation.
This slide introduces the course instructors, Abhishek Bansal and Pukhraj Parikh, outlining their backgrounds in technology and analytics.
This instructional slide outlines the recommended approach for the course, advising students to start with the basics of LangChain and finish the course completely.
Recent signals indicate that some libraries and API parameters have become deprecated, leading to debugging difficulties during practical application.
One signal suggests that provided Jupyter notebooks may contain code that no longer functions correctly in current environments.
The curriculum aligns well with the goals of aspiring AI developers and data scientists.