# Learn LangChain: AI & RAG

> Build LLM Apps with Python

Canonical page: [https://chrome-stats.com/d/id6794570808](https://chrome-stats.com/d/id6794570808)

## Overview

- **ID:** `id6794570808`
- **Platform:** Apple
- **Type:** Apple app
- **Status:** Available
- **Publisher:** Shahbaz Khan
- **Category:** Education
- **Rating:** 1.00 / 5 (1 ratings)
- **Version:** 1.0
- **Last updated:** 2026-08-03
- **First published:** 2026-08-03
- **Size:** 169 MB
- **Data as of:** 2026-09-06
- **Store listing:** [Apple App Store](https://apps.apple.com/us/app/learn-langchain-ai-rag/id6794570808)
- **Website:** [https://codemeals.com/](https://codemeals.com/)
- **Privacy policy:** [https://www.termsfeed.com/live/f10a4efe-1d7e-4b06-9feb-623c0f1aa669](https://www.termsfeed.com/live/f10a4efe-1d7e-4b06-9feb-623c0f1aa669)

## Description

Learn LangChain through practical lessons, quizzes, coding examples, RAG projects, AI agents, vector databases, and production-ready LLM patterns.

Learn LangChain and build modern AI applications with practical, structured lessons.Learn how large language model apps work—from prompt templates and chat models to chains, agents, retrieval-augmented generation, and production deployment.

Whether you are beginning with LangChain or expanding your LLM engineering skills, Learn LangChain helps you study the concepts and patterns used to build real AI products.

WHAT YOU’LL LEARN

- LangChain fundamentals and setup
- LLMs, chat models, prompts, and structured output
- LCEL and composable application chains
- Memory, conversation workflows, and state
- Document loaders, text splitters, embeddings, and vector stores
- Retrieval-augmented generation, or RAG
- Advanced RAG patterns, including Graph RAG and Agentic RAG
- AI agents, tools, ReAct workflows, and multi-agent systems
- LangGraph concepts, nodes, state, and workflows
- LangSmith, LangServe, templates, observability, and debugging
- Vector databases, semantic search, and retrieval pipelines
- AI evaluation, testing, quality measurement, and RAG evaluation
- Streaming, caching, error handling, scaling, and production architecture
- LoRA and efficient LLM fine-tuning
- vLLM, model serving, inference, and deployment concepts
- CrewAI and modern multi-agent application patterns
- MCP concepts and connected AI workflows

BUILD HANDS-ON PROJECTS

Apply what you learn through practical projects, including:

- Q&A bots with RAG
- Multi-document research agents
- AI customer support systems
- Vector search and retrieval pipelines
- Tool-using AI agents
- Advanced multi-agent systems
- Real-time RAG workflows
- Production-oriented LLM applications

LEARNING FEATURES

- Bite-sized lessons that build from fundamentals to advanced topics
- Python examples with JavaScript support
- Quizzes, challenges, tips, and good-to-know explanations
- Hands-on project-based learning
- Progressive learning paths for beginners and experienced developers
- Cheat sheets for LangChain fundamentals, RAG, and agents
- Progress tracking to help you continue your learning journey

Start your LangChain Journey Today !

## Rankings

- #976,568 — Overall

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Source: [Chrome-Stats](https://chrome-stats.com/d/id6794570808)