LLM App Development: Building Powered Applications with LangChain
Master the fundamentals of LangChain to build LLM-powered applications. This beginner-friendly course guides you through prompt templates, chains, agents, conversational memory, and document loaders using Python and OpenAI.
Course Overview
Welcome to LLM App Development: Building Powered Applications with LangChain. This hands-on course is designed to take you from a beginner to confidently building LLM-powered applications using the LangChain framework.
What You'll Learn
- LLMs vs ChatModels: Understand the core distinctions between standard text-completion models and structured, message-based chat models.
- Prompt Templates: Design reusable and dynamic blueprints to efficiently format inputs for language models.
- Chains: Construct execution pipelines linking prompts, LLMs, and other utility chains.
- Agents and Tools: Build systems where the LLM dynamically decides which actions to take and external tools to execute.
- Memory: Implement conversational memory to persist state across multiple interactions.
- Document Loaders: Load external data source files to perform custom question-answering.
Prerequisites
- Basic proficiency in Python programming.
- An active OpenAI API key for running model interactions.
- A development environment (like VS Code) set up on your machine.
Tools & Technologies
- Python
- LangChain
- OpenAI API
- VS Code
Course Content
Introduction to LangChain and Model Interfaces
Understanding LLMs and ChatModel Interfaces
Creating Dynamic Inputs with Prompt Templates
Building Workflows: Chains, Agents, and Memory
The Probabilistic Foundations of LLMs and LangChain Chains
Empowering Models with Agents, Tools, and Conversational Memory
This course includes
- 16m on-demand video
- 4 lessons across 2 chapters
- 2 interactive quizzes
- Access on mobile and desktop
- Source playlist on YouTube
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