Chunking Strategies for RAG explained with a real NestJS + Claude Code project. This is Phase 4 (AI Data Engineering) of the AI Engineer 360 Course, where we build production-ready AI systems from scratch.

Most beginners upload an entire PDF to an LLM and expect accurate answers. Production AI systems don't work like that. They split documents into intelligent chunks before retrieval, improving accuracy, reducing hallucinations, and saving tokens.

In this video you'll learn:
What Chunking is
Why Chunking is essential for RAG systems
Semantic Chunking
Recursive Chunking
Parent-Child Retrieval
Real-world AI applications and enterprise use cases

Practical Project
Using Claude Code and NestJS, we build a production-ready Chunking Engine with a clean, modular architecture that will become the foundation for the upcoming RAG videos in this course.

If you want to build enterprise AI applications instead of simple AI demos, this is one of the most important lessons in the entire AI Engineer 360 series.

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CONNECT & LEARN WITH US
Visit Our Website:
https://www.thetechzeen.com/

Source Code:
https://github.com/farzeen-ali/AI-Chunking-Strategies

Full Playlist (AI ENGINEER 360°):
https://www.youtube.com/playlist?list=PL5OhSdfH4uDtyUmg0r-F5UeQ9sineyE9t

Get Lecture Notes (GitHub):
https://github.com/farzeen-ali/AI-Engineer-360

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