Master Structured Prompting & JSON Outputs for AI Agents | End-to-End Python Tutorial скачать в хорошем качестве

Master Structured Prompting & JSON Outputs for AI Agents | End-to-End Python Tutorial

Description: Unlock the full potential of Large Language Models (LLMs) with Structured Prompting! In this end-to-end tutorial for AI engineers and developers, we dive deep into transitioning from unpredictable, free-form text prompts to reliable, machine-readable structured outputs like JSON, Pydantic models, and Mermaid graphs Whether you are building autonomous AI agents, automating data extraction, or preparing for your next AI engineering interview, this video covers all the core design concepts, coding implementations, and best practices you need to know. What you will learn in this video: The Shift to Bounded Reasoning: Why traditional Chain-of-Thought (CoT) prompting is being replaced by structured frameworks to reduce "reasoning drift" and token costs Advanced Frameworks (BRAID & STROT): Discover how the BRAID framework uses Mermaid diagrams to improve performance-per-dollar by up to 74x, and how the STROT framework enables robust data interpretation with feedback-driven refinement loops Python & LangChain Implementation: Hands-on code examples using the OpenAI API, Pydantic schemas, and LangChain's create_agent (ProviderStrategy vs. ToolStrategy) Automated Error Handling: How to build self-correcting AI pipelines using retry loops for malformed outputs and schema validation errors Data Security: How to protect sensitive information and PII in your LLM prompts using the open-source Prompt Sentinel library Interview Prep: Key design and development questions to help you ace your next AI Engineer interview. #StructuredPrompting #PromptEngineering #AIAgents #LangChain #JSONPrompting #LLM #Python #MachineLearning #ArtificialIntelligence #Pydantic #OpenAI #GPT4 #BRAIDFramework #STROTFramework #AIEngineer