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.
a free account unlocks the core curriculum tier · no card
Related course lessons
- Trace the evidence before changing the prompt →
22 min · Premium
Foundations of Forward Deployed Engineering · syllabus - Write the evaluation before the feature, or you will never know if it works →
25 min · Premium
Foundations of Forward Deployed Engineering · syllabus - Update evidence without mixing index generations →
24 min · Premium
Harness Engineering for FDEs · syllabus
Related concepts
Practise this in questions
RAG & Agent System DesignHow would you chunk documents for retrieval, and how do you know your chunking is good? →easy · FreeRAG & Agent System DesignA chunking change shipped last week. Aggregate recall barely moved, and support says answers got worse. Find the regression. →hard · PremiumRAG & Agent System DesignWalk me through a RAG pipeline end-to-end, and tell me where it usually breaks →easy · FreeRAG & Agent System DesignKeyword search vs vector search: what does each actually buy you in a RAG system? →easy · FreeLLM & GenAI FundamentalsWhat are embeddings, and how do they make semantic search work? →easy · FreeLLM & GenAI FundamentalsWhy is the dot product the similarity score in attention and embeddings, and when should you normalize to cosine? →medium · Free