LangChain MasterClass: Build 15 LLM Apps with Python

Packt organizes this course around a series of LangChain applications, including question answering, content workflows and document extraction. Dedicated modules introduce models, prompts, memory, data connections, chains and agents. Some lessons name Llama 2, so learners should compare the taught stack with current model options.

What you will learn

• Connect LangChain models, prompts and memory.
• Build an application that retrieves or extracts information.
• Explain where a chain or agent fits a project.

Who this suits

Intermediate learners with basic Python, machine-learning familiarity and experience with an AI framework or service such as OpenAI or Hugging Face.

Before you enroll

Check package versions, API access and which examples have been refreshed. Treat employment-related demonstrations as technical examples that need separate legal, fairness and human-review decisions before workplace use.

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Put it into practice

Adapt a document-extraction example to fictional invoices. Validate each returned field against the source and record when the model should return an unknown value.

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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