Learn how to build real-world AI agents using frameworks like LangChain, LangGraph, CrewAI, and AutoGen. This playlist covers agent architectures, workflows, tool calling, memory, and real-world use cases — from beginner to advanced. Perfect for AI engineers, developers, and system design learners. #AIAgents #GenerativeAI #LangChain #AIEngineering
Curated by: Naveen Tech Hub (38 videos)
🚀 Want reliable, production-ready LLM outputs instead of unpredictable text and broken JSON? In this quick 4-minute video, we explore how structured LLM outputs evolved from simple prompt-based JSON to schema-driven responses using LangChain and Pydantic. You’ll learn: ✅ Why prompt-based JSON can be unreliable ✅ What JSON mode solves — and what it doesn’t ✅ How schemas make LLM responses more predictable ✅ How LangChain `with_structured_output()` works ✅ Why Pydantic is useful for validation and type safety ✅ The idea behind production-ready structured output pipelines 💡 Simple idea: Raw LLM Output → JSON → Schema Enforcement → Pydantic Validation → Reliable Application Data Structured output is especially useful when building: • AI agents • RAG applications • Data extraction pipelines • APIs and microservices • Document-processing systems • Production GenAI applications If you're learning LangChain, Pydantic, Generative AI, RAG, or Agentic AI, this quick explanation will help you understand why structured output is such an important production pattern. Subscribe to *Naveen TechHub* for beginner-friendly, practical AI, Python, LangChain, Cloud, DevOps, and software engineering tutorials. #LangChain #Pydantic #StructuredOutput #GenerativeAI #LLM #Python #AIEngineering #AgenticAI #RAG #NaveenTechHub