Why Text splitters in Prompt Engineering / GenAI? - Purpose & Use Cases

The Big Idea

What if you could instantly find the exact part of a huge text without reading it all?

The Scenario

Imagine you have a huge book or a long article, and you want to find specific information quickly. Trying to read it all at once or searching manually is like looking for a needle in a haystack.

The Problem

Manually breaking down large texts into smaller parts is slow and tiring. It's easy to miss important sections or cut sentences awkwardly, making it hard to understand the meaning later.

The Solution

Text splitters automatically chop big texts into neat, meaningful chunks. They keep sentences whole and organize content so machines and people can handle it easily and quickly.

Before vs After

✗ Before

text = open('bigfile.txt').read()
chunks = []
for i in range(0, len(text), 1000):
    chunks.append(text[i:i+1000])

✓ After

from text_splitter import split_text
chunks = split_text(text, chunk_size=1000, keep_sentences=True)

What It Enables

Text splitters make it easy to process and understand large texts, enabling faster search, analysis, and smarter AI responses.

Real Life Example

When you ask a voice assistant a question about a long document, text splitters help the AI find the right part quickly to give you a clear answer.

Key Takeaways

Manual text handling is slow and error-prone.

Text splitters break text into clear, meaningful pieces automatically.

This helps machines and people work with large texts easily and efficiently.

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