10 related articles

A systematic breakdown of LangChain's six core modules (Models/Prompts/Chains/Memory/RAG/Agent) and LangGraph's state graph, persistence, and HITL — with production deployment tips.

A deep dive into LangChain's four core modules: LangChain components, LangGraph orchestration, Deep Agents, and LangSmith. Build your first Agent from scratch.

Learn LangChain Prompt templates and prompt engineering to build a customizable AI assistant like JARVIS. Covers LLM vs Chat Model differences with practical examples.

Frontend hiring now treats AI capabilities as a core assessment, covering RAG knowledge bases, AI Agent development, and LangChain.js engineering. Learn how LangChain.js + Nuxt.js helps frontend developers build memory- and retrieval-capable AI full-stack apps.

A deep dive into LangChain 0.3's module architecture, message abstraction, prompt templates, output parsers, LCEL chains, LangSmith tracing, and LangGraph for mastering LLM application development.

A systematic guide to LangChain LLM application development, covering environment setup, core components (RAG, Chain, Memory), and Agent development to help developers master LLM app building.
TutorialsDetailed guide to LangChain core modules including prompt templates, output parsers, Chain invocation, LCEL expression language, and LangSmith tracing tools for LLM application development.
TutorialsLearn LangChain FewShotPromptTemplate: core parameters, implementations for text completion and chat models, and practical use cases like batch file renaming to reduce LLM hallucinations.
TutorialsA deep dive into LangChain framework essentials, clarifying the key differences between LLMs and Agents, covering Agent development, DeepAgent advanced topics, and learning paths.
TutorialsA deep dive into LangChain prompt templates covering ChatPromptTemplate basics, Few-Shot prompting, and Chain-of-Thought techniques with RAG examples to build high-quality prompt pipelines.