02:04 Explaining Fine-tuning and Its Limitations, Emphasizing the Need to Avoid It When Possible 02:20 Introduction to the Principles and Applications of Retrieval-Augmented Generation (RAG) 02:36 Discussion of the Definition and Examples of Agentic AI Workflows 03:23 Brief Exploration of Multi-Agent Workflows and Future Prospects for AI 03:52 Open-Ended Question: What Limitations Does a Base Model Face When Used Alone? 09:39 LLMs May Perform Poorly on Specific Tasks, Especially When Lacking Domain-Specific Knowledge 11:22 Issues with LLMs Handling Limited Context and Challenges in Long-Text Processing 13:05 Explaining the "Needle in a Haystack" Problem: Difficulties in Extracting Information from Large Texts 14:32 Discussion of RAG: Its Advantages as an LLM Augmentation Mechanism and Long-Term Potential 16:42 Two Main Dimensions for Improving LLMs: Base Model Enhancements vs. Application Engineering 18:11 Beginning the Discussion on Prompt Engineering: Its Importance and Impact 21:49 Basic Prompt Design Principles: Optimizing Output Through Clear Instructions 24:01 Chain-of-Thought (CoT) Prompting: Breaking Down Tasks to Improve Model Performance 25:37 Prompt Templates: Enabling Scalability and Personalized Applications 27:48 Comparison and Applications of Zero-Shot vs. Few-Shot Prompting 32:15 Chaining Complex Prompts: Optimizing Workflows and Simplifying Debugging 37:56 Methods for Evaluating Prompts: Human Rating and Using LLMs as Judges 41:20 Drawbacks of Fine-Tuning: High Data Requirements, Overfitting, and Cost 42:50 Advantages of Fine-Tuning: Suitable for Domains Requiring High Precision 44:51 Core Concepts of RAG and Its Role in Addressing LLM Limitations 47:41 How RAG Works: Embeddings, Vector Databases, Retrieval, and Prompt Integration 50:02 Advanced RAG Techniques, Such as Chunking and Hypothetical Document Embeddings (HyDE) 53:53 Agentic AI Workflows: Moving Toward Autonomous and Specialized Systems 57:59 Paradigm Shift in Software Engineering: From Deterministic to Probabilistic Thinking 1:03:51 Enterprise Workflow Case Study: Using Generative AI Agents to Optimize Credit Risk Memos 1:07:01 Core Components of Agents: Prompts, Context Management (Memory), and Tools (APIs) 1:10:17 Different Levels of Agent Autonomy: From Hard-Coded Steps to Autonomously Creating Tools 1:12:20 Model Context Protocol (MCP) vs. Traditional APIs and Its Advantages 1:17:40 Step-by-Step Execution Example: An Intelligent Travel Agent 1:19:13 Evaluating Agentic AI Performance: End-to-End, Component-Level, Objective, and Subjective Metrics 1:25:34 Case Study: Building and Evaluating an AI Agent for Customer Support 1:34:26 Multi-Agent Workflows: Benefits of Parallel Processing and Component Reuse 1:35:44 Case Study Discussion: Multi-Agent System Design for Smart Home Automation 1:43:17 Future Trends in AI: Plateau Period, Architecture Search, Multimodality, and Multi-Method Collaborative Learning
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