Course Outline

Introduction to AI-Augmented SQL

  • Overview of AI integration in data systems
  • Evolution from traditional SQL to AI-assisted querying
  • Key enterprise use cases and benefits

Understanding LLMs in SQL Context

  • How LLMs interpret and generate structured queries
  • Comparison of GPT, LlaMA, DeepSeek, Qwen, and Mistral for SQL applications
  • Fine-tuning models for database interaction

Natural Language to SQL (NL2SQL) Systems

  • Architectures and approaches for NL2SQL
  • Building and deploying text-to-SQL pipelines
  • Evaluating query accuracy and user intent

AI-Assisted Query Optimization

  • Using AI to detect and correct inefficient queries
  • LLM-based query rewriting for performance
  • Integrating AI optimization into PostgreSQL and SQL Server

Security, Governance, and Auditability

  • Controlling access to AI-generated queries
  • Ensuring explainability and compliance
  • Implementing AI governance in enterprise data systems

LLM Integration and Orchestration

  • Connecting SQL engines with AI APIs
  • Using frameworks such as LangChain and LlamaIndex
  • Deploying AI components in hybrid and cloud architectures

Practical Implementation Labs

  • Setting up AI-SQL connections and test environments
  • Creating and evaluating AI-generated queries
  • Measuring performance improvements with AI optimization

Future Trends and Enterprise Adoption Strategies

  • AI-native database systems and SQL evolution
  • Integration with data lakes, BI tools, and pipelines
  • Building internal AI query assistants for organizations

Summary and Next Steps

Requirements

  • An understanding of SQL fundamentals
  • Experience with database administration or data engineering
  • Basic knowledge of AI or machine learning concepts

Audience

  • Data engineers and database administrators
  • Enterprise architects and analytics leads
  • AI integration and platform engineering teams
 21 Hours

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