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GenAI / LLM · Beginner

Document ingestion & chunking

Turn raw documents into clean, searchable chunks that form the foundation of a production retrieval-augmented generation system.

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Project blueprint

What you'll build

RAG quality starts before embeddings and models. This project builds the ingestion layer that accepts raw files, extracts useful text, preserves metadata, and produces chunks that retrieval can actually use.

You will compare chunking strategies and learn how document structure affects the answers generated later in the pipeline.

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Included build referenceBuild reference — documents, metadata, and chunk boundaries
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Project overview

Understand the build here. Learn and access the assets in the LMS.

This page covers what the project does, what you need, and how the build comes together. The LMS contains the complete lessons, source code, circuit diagrams, and deployment materials.

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Before you start

Build requirements

01Python development environment
02A small set of source documents
03A terminal and code editor
From setup to working system

Build sequence

Follow the steps in order, verify each checkpoint, and finish with a working project you can explain and extend.

01

Collect and inspect source documents

Choose representative files and identify the structure that needs to be preserved.

02

Build the ingestion pipeline

Load documents, extract text, and attach useful metadata.

03

Compare chunking strategies

Test fixed, recursive, and structure-aware chunking against realistic queries.

04

Package clean chunks

Write a consistent output format ready for embeddings and retrieval.

Build the next checkpoint

Stop collecting tutorials. Ship a working build.

Use this project as a concrete piece of your embedded, IoT, or edge AI portfolio.

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