← All courses
AI
Lang Flow Tutorial
Lang Flow Tutorial
4.7 (377)6,777 learners15 lessons39m
Curriculum
Topic
- Why Building a Simple LLM Wrapper Is No Longer Enough2:32
- LangChain 1.0 vs LangGraph 1.0: Developer Experience Layer vs Orchestration Engine2:58
- The Anatomy of an LLM Call: Tokens, Context Windows, and Why Statelessness Is the Core Problem2:29
- What Agentic AI Actually Means: How Agents Reason, Plan, Use Tools, and Autonomously Complete Tasks2:42
- Evolution of React Agents: From Prompt-Based Reasoning to Native Function Calling to LangGraph-Native Architecture1:55
- LCEL — The Pipe Operator That Replaced Chains of Dictionaries3:14
- Runnable Protocol — The Unified invoke(), batch(), and stream() Contract Every Component Shares3:14
- Runnable Parallel — Feeding One Input to Multiple Chains Simultaneously1:47
- Prompt Templates and Few-Shot Chat Prompts — Dynamic Instructions with Real Examples3:04
- Output Parsers and Structured Output with Pydantic — Turning Raw LLM Text into Typed Objects2:42
- Multi-Provider LLM Switching — Swapping OpenAI for Anthropic or Hugging Face Without Rewriting Logic2:29
- Streaming and Async Chains — Handling Token-by-Token Output in Real-Time User-Facing Applications2:45
- The RAG Pipeline from End to End — How Raw Documents Become Grounded LLM Answers2:53
- Document Loaders, Text Splitters, and Chunking Strategies — Why How You Cut Text Changes Everything2:00
- Embeddings and Vector Stores — Turning Meaning into Math and Querying It at Scale2:09