Core

Chunking Strategies

Chunking is how you split documents into the units you embed and retrieve, and it quietly sets the recall ceiling for your entire RAG system. Get the size, boundaries, and metadata wrong and no reranker or prompt can recover the answer that never got retrieved.

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RELATED CONCEPTS

Retrieval-Augmented Generation (RAG)Embeddings & Vector Representations Vector Databases Reranking and Two-Stage Retrieval

LESSONS THAT TEACH THIS

Foundations of Forward Deployed EngineeringTrace the evidence before changing the prompt22 min→Foundations of Forward Deployed EngineeringWrite the evaluation before the feature, or you will never know if it works25 min→

PRACTICE THIS IN REAL QUESTIONS

RAG & Agent System DesignWalk me through a RAG pipeline end-to-end, and tell me where it usually breaks→RAG & Agent System DesignHow would you chunk documents for retrieval, and how do you know your chunking is good?→ML System Design (Product)Design a music recommendation system (Spotify-style).→RAG & Agent System DesignKeyword search vs vector search: what does each actually buy you in a RAG system?→LLM & GenAI FundamentalsWhat are embeddings, and how do they make semantic search work?→RAG & Agent System DesignHow would you evaluate a RAG system before letting a customer launch it?→