Using AI to generate SQL queries to power a RAG (retrieval augmented generation) application is a powerful way to add an AI layer to your product. I have built several applications like this for my enterprise software clients and today I'll share the fundamentals of building such apps with you.
This is an in-depth hands-on tutorial about building AI-powered applications with the power of AI-generated SQL to answer user questions.
📚 Resources:
- Code and diagram used in video: https://github.com/VoloBuilds/ai-sql-query-rag
🔧Tools Used:
- OpenAI GPT-4o-mini
- Cursor + Claude Sonnet 3.5
- Postgres
- MERN (Mongo, Express, React, NodeJS)
- Tailwind, ShadCN
🚀 In This Video, You'll learn:
- How to build RAG systems
- How to use AI with your own data
- How to generate SQL with AI
- How to build enterprise AI-powered applications
- AI to query a database
- AI-powered chatbot architecture
- What is RAG (retrieval augmented generation)
- Vector RAG vs Query RAG
- How to build a custom chatbot
- Tips for building RAG pipelines
- Limitations of AI RAG
💡 Perfect for Viewers Interested in:
- Full Stack software development
- Best AI applications
- Business AI usecases
- AI-generated SQL
- Software Development
- Coding with AI
- Learning about the latest AI tech
- Generative AI
- GenAI chatbots
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💬 Questions or Feedback? Drop your thoughts in the comments below, and I'll be sure to get back to you!
Chapters
00:00 - Intro to RAG
01:29 - Architecture
09:15 - Coding
19:23 - Prompt Engineering
29:32 - Tips & Limitations