The Complete LangChain & LLMs Guide

Packt develops LangChain from environment setup through prompts, output parsers and LCEL. Document splitting, embeddings and vector stores lead into RAG and LangGraph foundations, followed by question-answering and research-assistant projects. The outline labels one image-to-text example as legacy material.

What you will learn

• Compose model calls with prompts and output parsers.
• Build document-loading and retrieval stages.
• Explain how a LangChain workflow relates to a LangGraph project.

Who this suits

Intermediate developers and AI practitioners seeking more substantial language-model application skills, as described by Packt.

Before you enroll

Check which modules use current APIs and which remain as legacy examples. Compare the course’s model and vector-store access requirements with your available budget.

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Put it into practice

Create a small retrieval workflow with a structured output parser. Test a malformed model response and document how the application rejects or repairs it.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

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