A component-by-component route to small LangChain applications and a first graph-based agent workflow.
Start with the role of LangChain in generative-AI applications, then examine components, model interfaces, prompts, structured outputs, and parsers before moving to a LangGraph agentic workflow. CampusX provides the selected demonstrations and code context; package names and APIs can change, so treat the exercises as patterns to verify rather than immutable production instructions. The course rewards explicit schemas, test inputs, and observable state transitions.
Identify the responsibilities of models, prompts, chains, tools, and parsers in an LLM application. Design a prompt and output schema that downstream code can validate. Explain why parsing and failure handling belong in an application boundary. Sketch a small LangGraph workflow with state, steps, and a human review point.
Intermediate Python, virtual-environment basics, and familiarity with calling an API are recommended. An API key may be needed for optional experiments; do not commit secrets.
Learners with basic familiarity who are ready to build practical, independent skills
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
7 lessons · Intermediate · Full course.
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