Text chunking strategies in Prompt Engineering / GenAI - Full Explanation

Introduction

When working with large amounts of text, it can be hard to process or understand everything at once. Breaking text into smaller, manageable pieces helps computers and people handle information more easily and accurately.

Explanation

Fixed-size chunking

This method splits text into equal-sized pieces, like cutting a long rope into equal segments. It does not consider the meaning or structure of the text, just the length. This makes it simple but can cut sentences or ideas in awkward places.

Fixed-size chunking divides text by length without considering meaning or sentence boundaries.

Sentence-based chunking

Here, text is divided by sentences. Each chunk contains one or more complete sentences, preserving meaning better than fixed-size chunks. This helps keep ideas intact but can result in chunks of varying sizes.

Sentence-based chunking keeps sentences whole to preserve meaning.

Semantic chunking

This strategy breaks text based on meaning or topics. It groups related sentences or paragraphs together, so each chunk covers a specific idea. This approach helps computers understand context but requires more complex analysis.

Semantic chunking groups text by meaning to keep related ideas together.

Overlap chunking

Overlap chunking creates chunks that share some text with neighboring chunks. This overlap helps maintain context between chunks, reducing the chance of losing important connections when processing each piece separately.

Overlap chunking shares text between chunks to preserve context.

Real World Analogy

Imagine you have a long storybook to share with friends. You can cut it into equal pages, split it by chapters, group parts by themes, or share some sentences twice between friends to keep the story connected.

Fixed-size chunking → Cutting the storybook into equal pages without caring about sentences or chapters

Sentence-based chunking → Splitting the storybook by chapters or paragraphs so each friend gets a complete part

Semantic chunking → Grouping story parts by themes like adventure or mystery to keep related ideas together

Overlap chunking → Sharing some sentences twice between friends so they don’t miss important connections

Diagram

Diagram

┌───────────────┐
│   Full Text   │
└──────┬────────┘
       │
       ▼
┌───────────────┐   ┌───────────────┐   ┌───────────────┐   ┌───────────────┐
│ Fixed-size    │   │ Sentence-based │   │ Semantic      │   │ Overlap       │
│ chunks        │   │ chunks        │   │ chunks        │   │ chunks        │
│ [equal parts] │   │ [by sentences]│   │ [by meaning]  │   │ [shared text] │
└───────────────┘   └───────────────┘   └───────────────┘   └───────────────┘

This diagram shows the full text being divided into four types of chunks: fixed-size, sentence-based, semantic, and overlap.

Key Facts

Fixed-size chunking → Splits text into equal-length pieces without considering meaning.

Sentence-based chunking → Divides text by complete sentences to keep ideas intact.

Semantic chunking → Groups text by meaning or topic to preserve context.

Overlap chunking → Creates chunks that share some text to maintain connections.

Common Confusions

Thinking fixed-size chunks always keep sentences whole.

Thinking fixed-size chunks always keep sentences whole. Fixed-size chunking cuts text purely by length, so sentences can be split across chunks.

Believing semantic chunking is simple to implement.

Believing semantic chunking is simple to implement. Semantic chunking requires understanding text meaning, which needs advanced analysis and is more complex.

Summary

Breaking text into chunks helps manage and understand large amounts of information.

Different chunking strategies balance simplicity and preserving meaning in various ways.

Choosing the right chunking method depends on the goal and how much context needs to be kept.