LangChain Proficiency: Advanced Prompting, RAG with Vector Databases, and Codebase Navigation

Master LangChain for building sophisticated GenAI applications: from advanced prompting and RAG to codebase navigation and customization.

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Fundamentals of LangChain and LLMs for Application Development

Unit 1: LangChain Core Concepts

Unit 2: LLM Fundamentals for Engineers

Unit 3: Setting Up Your LangChain Environment

Unit 4: Advanced Setup and Tooling

Advanced Prompting Techniques with LangChain

Unit 1: Chain of Thought (CoT) Prompting in LangChain

Unit 2: ReAct Framework with LangChain

Unit 3: Few-Shot Prompting Strategies

Building LangChain Applications: Chains, Agents, and Memory

Unit 1: LangChain Chains: Sequential Data Processing

Unit 2: LangChain Agents: Intelligent Interaction

Unit 3: LangChain Memory: Maintaining Context

Document Handling and Output Parsing in LangChain

Unit 1: Document Loaders in LangChain

Unit 2: Text Splitting Techniques

Unit 3: Output Parsing Strategies

Retrieval-Augmented Generation (RAG) with Vector Databases

Unit 1: Vector Databases and LangChain Integration

Unit 2: Building RAG Applications with LangChain

Unit 3: Optimizing RAG Pipelines

LangChain Codebase Navigation and Customization

Unit 1: Exploring the LangChain Core

Unit 2: Customizing LangChain Components

Unit 3: Contributing to LangChain