Why ChatGPT Doesn't Use RAG (And What It Does Instead) AIML Hindi 10 RAG2

Infinite context windows were supposed to solve AI memory.
They didn't. Naive RAG breaks in production. Here's the surgical fix.
In this video, you'll learn:

The Token Trap — why giving an AI more context actually degrades answer accuracy past a critical threshold
Context Rot — the U-shaped attention valley where data buried in the middle of a prompt gets ignored
The Distractor Problem — why similar-but-wrong documents destroy AI reasoning more than random noise
The ChatGPT Memory Myth — why production systems use a surgical 4-layer context architecture, not vector RAG
GraphRAG, RAPTOR, and PageIndex — the 3 next-generation architectures replacing naive chunking entirely

TIMESTAMPS
00:00 — Intro: Why RAG Breaks & What Comes Next
00:45 — The Token Trap: Why More Context Ruins Answers
01:30 — Context Rot: The Hidden Killer (U-Shaped Attention)
02:15 — The Distractor Problem & Semantic Retrieval
03:00 — The ChatGPT Memory Myth (4-Layer Context Architecture)
03:45 — Treatment 1: Hybrid Search (BM25 + Vector RRF)
04:30 — Treatment 2: Re-Ranking (Cross-Encoder)
05:15 — Treatment 3: Query Transformation with HyDE
06:00 — Architecture Shift 1: GraphRAG (Knowledge Graphs)
06:45 — Architecture Shift 2: RAPTOR (Hierarchical Memory Trees)
07:15 — The Vectorless Frontier: PageIndex
07:45 — The 4 Phases of AI Retrieval Evolution
08:00 — The Surgical Blueprint: Key Takeaways & Recap
This video is for AI engineers and developers who have built basic RAG and want to understand why it fails at scale — and how production systems at OpenAI and Anthropic actually work. No PhD required.
If this helped, like and subscribe for more AI/ML content simplified — @kaushalkrsna
#AI #MachineLearning #RAG #GraphRAG #RAPTOR #AIForBeginners #kaushalkrsna #LearnAI #AISimplified #MLBasics #ContextRot #HyDE #PageIndex #VectorDatabase #AIEngineering

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