Module
Module 3 of 6Lesson 1 of 2~22 min

Choose a chunking strategy

When a document is split badly, the assistant sees only part of the right answer and the user gets incomplete information. The lesson covers the main families of chunking strategies, explains what a PM should contribute to that decision, and how to phrase it as requirements the team can test.

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Lesson objective

By the end of this lesson, you will be able to choose a chunking strategy suited to your documents' structure and to the expected answer unit, and to phrase it as requirements the team can test.

Topics covered

  • chunking
  • document splitting
  • RAG chunking strategy
  • parent-child chunks

Where it fits

Chunk and index

How do documents become passages that retrieval can find?

Lessons in this module

  1. Choose a chunking strategy (this lesson)
  2. Understand embeddings and semantic search

What you will learn in the course

This lesson is part of the course Design a RAG architecture that fits your product

  • Choose, for an information need, between long context, data injected by the application, RAG and fine-tuning, and justify the choice by volume, update frequency, access rights and cost.
  • Specify the source inventory, exclusions, the metadata to capture and the document chunking strategy.
  • Design retrieval: semantic, keyword or hybrid search, reranking, contextual retrieval, number of passages and relevance threshold.
  • Specify the answer grounded in sources, how citations are displayed, how conflicting sources are handled and what happens when nothing is found.
  • Specify access filtering before the model reads any passage, and index freshness (resync, deletions, versions).
  • Build a set of 30 test questions that covers RAG failure modes and diagnose which stage caused a wrong answer.
  • Estimate the cost items of a RAG system and choose between a hosted tool, a managed knowledge base, a vector database and a custom build.

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