Text splitters in Prompt Engineering / GenAI - Full Explanation

Introduction

When working with large texts, it can be hard to process or analyze everything at once. Breaking text into smaller parts helps manage and understand the content better.

Explanation

Purpose of Text Splitting

Text splitting divides a long piece of text into smaller, manageable chunks. This makes it easier for computers or people to analyze, search, or summarize the content. It also helps avoid overload when processing large documents.

Splitting text helps handle large content by breaking it into smaller, easier parts.

Common Splitting Methods

Text can be split by sentences, paragraphs, or fixed lengths like characters or words. Each method suits different needs; for example, sentence splitting keeps meaning clear, while fixed-length splitting ensures uniform chunk sizes.

Different splitting methods serve different purposes depending on how the text will be used.

Handling Overlaps

Sometimes chunks overlap slightly to keep context between parts. This overlap helps maintain meaning when analyzing or generating responses from each chunk separately. Overlaps prevent losing important connections between text pieces.

Overlapping chunks keep context and improve understanding across split parts.

Applications in AI and Search

Text splitters are used in AI to feed smaller text pieces into models for tasks like summarization or question answering. They also help search engines index content efficiently by breaking documents into searchable segments.

Splitting text enables better AI processing and more effective search indexing.

Real World Analogy

Imagine trying to read a very long book all at once—it would be overwhelming. Instead, you read it chapter by chapter or page by page. Sometimes you reread a few lines from the previous page to remember the story better.

Purpose of Text Splitting → Reading a book chapter by chapter to avoid feeling overwhelmed

Common Splitting Methods → Choosing to read by chapters, pages, or paragraphs depending on how much you want to read at once

Handling Overlaps → Rereading a few lines from the previous page to keep the story clear

Applications in AI and Search → Using chapters or pages to find specific parts of a book quickly or to summarize the story

Diagram

Diagram

┌───────────────┐
│   Full Text   │
└──────┬────────┘
       │ Split into
       ▼
┌───────────────┐   ┌───────────────┐   ┌───────────────┐
│   Chunk 1     │   │   Chunk 2     │   │   Chunk 3     │
│ (e.g., para)  │   │ (e.g., para)  │   │ (e.g., para)  │
└───────────────┘   └───────────────┘   └───────────────┘
       ▲               ▲                   ▲
       │<----Overlap--->│                   │

This diagram shows a full text being split into smaller chunks with some overlap between parts to keep context.

Key Facts

Text splitter → A tool or method that breaks large text into smaller pieces.

Chunk → A smaller part of text created by splitting.

Overlap → A repeated section between chunks to maintain context.

Sentence splitting → Dividing text by sentences to keep meaning clear.

Fixed-length splitting → Dividing text into equal-sized pieces regardless of meaning.

Common Confusions

Thinking text splitting always cuts at sentence boundaries.

Thinking text splitting always cuts at sentence boundaries. Text can be split by sentences, paragraphs, or fixed sizes; not all methods keep sentences whole.

Believing overlap means repeating the entire previous chunk.

Believing overlap means repeating the entire previous chunk. Overlap is only a small part of the previous chunk to keep context, not the whole chunk.

Summary

Text splitters break large texts into smaller, manageable chunks to make processing easier.

Different splitting methods exist, such as by sentences or fixed lengths, each useful for different tasks.

Overlapping chunks help keep context between parts, improving understanding and analysis.

Practice

1. What is the main purpose of a text splitter in AI applications?

easy

A. To translate text into different languages

B. To generate new text from a prompt

C. To break long text into smaller, manageable pieces

D. To summarize text into a single sentence

Solution

1. Step 1: Understand the role of text splitters

   Text splitters are designed to divide long text into smaller parts for easier processing.

2. Step 2: Compare options to the definition

   Only To break long text into smaller, manageable pieces describes breaking text into smaller pieces, which matches the purpose of text splitters.

3. Final Answer:

   To break long text into smaller, manageable pieces -> Option C

4. Quick Check:

   Text splitter purpose = break text [OK]

Hint: Text splitters cut text into chunks for easier handling [OK]

Common Mistakes:

• Confusing splitting with translation
• Thinking splitters summarize text
• Assuming splitters generate new text

2. Which of the following is the correct way to set chunk size and overlap in a text splitter?

easy

A. chunk_size='100', overlap=20

B. chunkSize=100, overlap=20

C. chunk_size=100, overlap=twenty

D. chunk_size=100, overlap=20

Solution

1. Step 1: Identify correct parameter names and types

   Parameters should be named with underscores and numeric values for size and overlap.

2. Step 2: Check each option for syntax and type correctness

   chunk_size=100, overlap=20 uses correct parameter names and numeric values; others have wrong names or types.

3. Final Answer:

   chunk_size=100, overlap=20 -> Option D

4. Quick Check:

   Correct param names and numeric values = chunk_size=100, overlap=20 [OK]

Hint: Use underscores and numbers for chunk size and overlap [OK]

Common Mistakes:

• Using camelCase instead of snake_case
• Passing string instead of number for overlap
• Misspelling parameter names

3. Given the following Python code using a text splitter:

text = "Hello world! This is a test of text splitting."
chunk_size = 12
overlap = 4
splitter = TextSplitter(chunk_size=chunk_size, overlap=overlap)
chunks = splitter.split(text)
print(chunks)

What is the expected output?

medium

A. ["Hello world!", "world! This is", "This is a test", "a test of text", "of text splitting."]

B. ["Hello world! This", "This is a test of", "test of text splitting."]

C. ["Hello world! This is a test of text splitting."]

D. ["Hello", "world!", "This", "is", "a", "test", "of", "text", "splitting."]

Solution

1. Step 1: Understand chunk size and overlap effect

   Chunk size 12 means each piece has up to 12 characters; overlap 4 means next chunk starts 4 characters before previous ends.

2. Step 2: Apply splitting logic to the text

   Chunks are: "Hello world!" (12 chars), then start 8 chars in (12-4=8) at "world! This is", and so on, producing the listed chunks in ["Hello world!", "world! This is", "This is a test", "a test of text", "of text splitting."].

3. Final Answer:

   ["Hello world!", "world! This is", "This is a test", "a test of text", "of text splitting."] -> Option A

4. Quick Check:

   Chunk size 12 + overlap 4 = overlapping chunks [OK]

Hint: Chunk size limits length; overlap repeats last part [OK]

Common Mistakes:

• Ignoring overlap and making chunks non-overlapping
• Using wrong chunk sizes
• Returning entire text as one chunk

4. Consider this code snippet that tries to split text but raises an error:

text = "Sample text for splitting."
splitter = TextSplitter(chunk_size='10', overlap=3)
chunks = splitter.split(text)

What is the most likely cause of the error?

medium

A. chunk_size should be an integer, not a string

B. overlap cannot be less than 5

C. TextSplitter requires a minimum chunk_size of 20

D. The text variable is too short to split

Solution

1. Step 1: Check parameter types

   chunk_size is given as a string '10' instead of an integer 10, which causes a type error.

2. Step 2: Validate other options

   Overlap 3 is valid; no minimum chunk size of 20 is required; text length is sufficient.

3. Final Answer:

   chunk_size should be an integer, not a string -> Option A

4. Quick Check:

   Parameter type mismatch = chunk_size should be an integer, not a string [OK]

Hint: Use numbers, not strings, for chunk size and overlap [OK]

Common Mistakes:

• Passing chunk_size as string
• Assuming overlap minimum is 5
• Thinking text length causes error

5. You have a very long document and want to split it for an AI model that can only process 500 tokens at a time. You want some context overlap to keep meaning. Which approach best balances chunk size and overlap?

hard

A. Set chunk_size to 600 tokens and overlap to 0 tokens

B. Set chunk_size to 500 tokens and overlap to 100 tokens

C. Set chunk_size to 400 tokens and overlap to 200 tokens

D. Set chunk_size to 100 tokens and overlap to 50 tokens

Solution

1. Step 1: Understand model token limit and overlap purpose

   The model can process 500 tokens max; overlap adds repeated context to help understanding.

2. Step 2: Evaluate options for chunk size and overlap

   Set chunk_size to 500 tokens and overlap to 100 tokens uses chunk size 500 (max allowed) and overlap 100 (reasonable context). Others exceed limit or have too small chunks.

3. Final Answer:

   Set chunk_size to 500 tokens and overlap to 100 tokens -> Option B

4. Quick Check:

   Chunk size ≤ 500 with overlap for context = Set chunk_size to 500 tokens and overlap to 100 tokens [OK]

Hint: Keep chunk size at max limit, add moderate overlap [OK]

Common Mistakes:

• Exceeding model token limit
• Setting overlap too large or zero
• Using very small chunk sizes unnecessarily