Chunking, Indexing, and Preparing Documents for RAG

Learn how document chunking affects retrieval and why indexing strategy matters for usable RAG systems.

Explanation

Chunking splits source material into smaller pieces that can be embedded and retrieved efficiently.

Chunks should preserve enough context to be meaningful but stay focused enough for precise retrieval.

Good indexing captures both content and metadata, such as source, section title, date, and access level.

Why this topic matters in practice

In generative AI products, the model is only one part of the system. The surrounding workflow determines whether the output is useful, safe, and maintainable. This lesson matters because it helps you connect the idea to tasks such as tutoring, search, copilots, business assistants, and production automation.

Examples

A basic fixed-size chunker

The code below is intentionally concise so the underlying pattern stays clear. It focuses on the application logic you can reuse, even if you later switch model providers or deployment environments.

def chunk_text(text, size=120, overlap=20):
    chunks = []
    start = 0
    while start < len(text):
        end = start + size
        chunks.append(text[start:end])
        start += size - overlap
    return chunks

sample = "Generative AI can support drafting, summarization, retrieval, tutoring, coding, and automation."
for i, chunk in enumerate(chunk_text(sample, size=35, overlap=8), start=1):
    print(f"Chunk {i}: {chunk}")

How the coding section works

• This example chunks by character count, but production systems often chunk by sentence, heading, or token count.
• Overlap helps preserve context between neighboring chunks.
• Chunking strategy should match the structure of the source material.

Implementation advice

When turning this lesson into a real feature, think beyond the code snippet itself. Decide what inputs should be allowed, how you will validate outputs, how you will recover from errors, and how you will measure whether the feature is actually helping users. Those surrounding choices often determine whether an AI feature feels polished or unreliable.