Practical Lessons in Building Generative AI: RAG and Text to SQL

This video provides practical lessons for building generative AI applications. It covers two popular use cases, Retrieval Augmented Generation (RAG) and Text to SQL.

Contents:
0:00 Introduction
1:00 LLMs and Hallucinations
4:41 Introducing Retrieval Augmented Generation
7:02 Dewey, Cheetham, and Howe and the Limits of LLMs Based on Legal Research
13:32 Introducing Frosty and Building a Generative AI app
14:14 Evaluation and Model as a Judge
19:06 Ensembles
19:40 Reflection / Chain of Thought
20:41 Screening inputs
21:39 Feature Extraction
22:45 Human Expertise
25:05 Open AI O1 and Block World for Reasoning

Summary:
🚀 Generative AI’s Potential: Generative AI can streamline tasks but has limitations in accuracy/hallucination.
🔍 Retrieval-Augmented Generation (RAG): RAG can reduce hallucination by grounding LLMs with factual information.
⚖️ RAG Risks: The limits of RAG are illustrated with a case study on legal research.

📊 Text-to-SQL Applications: Walk through building a full system (not just a model) for accurate Text to SQL
This involves:
🧠 Model Evaluation: Model-based evaluation can help assess AI output quality effectively.
🔄 Ensemble Learning: Combining multiple models can enhance performance in generative AI applications.
💭 Reflection: Having models reflect on their prompts can improve performance.
🔍 Feature Extraction: Identifying the best features for a prompt can improve performance.
📈 Incorporating Expertise: Expert knowledge is crucial in guiding the AI’s reasoning and validating outputs.

━━━━━━━━━━━━━━━━━━━━━━━━━
★ Rajistics Social Media »
━━━━━━━━━━━━━━━━━━━━━━━━━