Brief

Multiple dev.to articles detail how to build AI agents using LangGraph, a workflow system from LangChain. The posts provide templates for common agent patterns, including Retrieval-Augmented Generation (RAG) for document querying, multi-tool agents that can plan and execute tasks, and human-in-the-loop workflows requiring user review. These templates illustrate LangGraph's architecture with nodes, edges, and state management for creating complex, stateful AI applications. AI

Retrieval-Augmented Generation (RAG) systems are a crucial technique for enhancing Large Language Models (LLMs) by allowing them to access and utilize external, up-to-date information. RAG addresses LLM limitations such as knowledge cutoffs and context window limits by retrieving relevant data before generating a response. This approach is distinct from fine-tuning, which modifies the model's behavior rather than its knowledge base. Building a RAG system involves two main pipelines: an ingestion pipeline for preparing and storing data, and a retrieval pipeline that fetches context for each user query. AI