Build real world applications with Large Language Models and LangChain!
InstructorJose PortillaThe curriculum follows a consistent pattern of providing specific exercises followed by dedicated solution videos across multiple modules.
Instruction covers essential building blocks including document loaders, text embedding, vector store retrievers, and various chain specializations.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This diagram outlines the five main sections of the LangChain course: Model IO, Data Connection, Chains, Memory, and Agents.
This slide outlines the Model IO section of the LangChain course, detailing how it handles basic LLM inputs and outputs while offering standardization for switching between different models.
This slide introduces the concept of Chains in LangChain, explaining how they allow linking the output of one model to the input of another for modular workflows.
This slide introduces Agents as the most powerful component of LangChain, highlighting how they utilize models, data connections, chains, and memory to reason through requests.
Learners report that many libraries, functions, and imports are deprecated or broken in current versions of LangChain, requiring manual troubleshooting.
Some signals suggest the content lacks depth regarding how inputs pass through specific steps or the nuances between different API variants.
The curriculum aligns well with the target of Python developers interested in LLM application development.
