RAG Architecture Deep Dive

You already know RAG from the Advanced path: embed documents, retrieve via vector search, pass to the LLM as context. That works -- until it doesn't. Wrong chunks, irrelevant results, missing connections for complex questions.

In this module, we go deep. You'll learn why Naive RAG hits its limits, which architectures are production-ready in 2026, and when RAG is even the right answer at all.

What to Expect

RAG Evolution -- From Naive RAG through Advanced RAG with Hybrid Search and Re-Ranking to Agentic RAG with iterative loops
Chunking Strategies -- Why fixed-size chunking is outdated and how Semantic Chunking and Parent-Child Chunking transform retrieval quality
Vector Databases -- Pinecone, Weaviate, pgvector, and Qdrant compared architecturally: when to use which DB, how to scale, what it costs
GraphRAG -- Why Knowledge Graphs + RAG excel at multi-hop reasoning and when pure vector search falls short
RAG vs. Long Context -- The key question of 2026: with 1M+ token context windows, do you even need RAG anymore?

Prerequisites

You should have completed the Advanced path. In particular, the modules on Embeddings, Context Strategies, and RAG Introduction are required. If you know what Cosine Similarity measures, how a RAG pipeline works, and what an embedding vector represents, you're ready.

*Learning Objective

After this module, you'll be able to evaluate RAG architectures, select the right chunking strategy for a use case, and make informed decisions about whether RAG, Long Context, or GraphRAG is the optimal solution. You'll be operating at Bloom's "Evaluate" and "Create" levels.

Let's start -- with a look at the evolution of RAG.

Reflect

RAG is far more than simple "document search" -- it is an architectural decision with many levers to adjust. In this module, you will dive deep into chunking strategies, vector databases, and the trade-offs between RAG and Long Context. Let us start with the evolution of RAG.