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LLM Application Development

Learn to build applications powered by Large Language Models using frameworks like LangChain and LlamaIndex. Develop chatbots, question-answering systems, and agents with Retrieval-Augmented Generation (RAG).

LLM Application Development

LangChain Fundamentals: Building Retrieval-Augmented Generation (RAG) Apps

Build AI Workflows with LangChain and Open-Source LLMs

Building AI Applications from Concept to Deployment

Connect AI to Custom Data with RAG

AI for Developers: Integrating LLMs with APIs and Prompts

No-Code Generative AI Application Building with Dify

Introduction to RAG: AI Answers from Your Documents

API Application Development with DeepSeek and Tongyi

LLM Apps for Data: Build a RAG Analytics Assistant

AI Agent Observability: LangSmith and Langfuse

Foundations of AI Agent Observability with Langfuse

Foundations of AI Agent Evaluation with LangSmith

Building GraphRAG Applications with LlamaIndex

Agentic RAG Fundamentals: Build AI Agents with LangGraph

Building AI Agents with LlamaIndex and RAG

LangGraph Studio: Build and Debug AI Agent Workflows

LangChain and LangGraph: Building Stateful AI Agents

Foundations of RAG Pipelines and LLMOps

Build a Local Private AI Stack: Self-Hosted LLMs and RAG

Building LLM Apps with TypeScript and LangChain.js

Java AI Development: LangChain4j and Spring AI

Structured LLM Outputs: JSON, Pydantic, and Validation

LLM Function Calling: Connect AI to APIs and Databases

LlamaIndex Fundamentals: Build AI Agents and RAG Apps