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

The Big Idea

Discover how breaking text into smart pieces can unlock hidden insights effortlessly!

The Scenario

Imagine trying to read a huge book all at once without breaks or chapters. It feels overwhelming and confusing to find important parts.

The Problem

Manually splitting long texts into meaningful parts is slow and tiring. You might miss key ideas or cut sentences awkwardly, making understanding harder.

The Solution

Text chunking strategies automatically break large texts into smaller, clear pieces. This helps machines and people focus on important bits without losing context.

Before vs After

✗ Before

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

✓ After

chunks = smart_chunker.split(text)
# splits by sentences or topics, not just fixed size

What It Enables

It enables smooth handling of large texts for better analysis, search, and understanding by AI and humans alike.

Real Life Example

When reading long legal documents, chunking helps highlight sections like terms, conditions, and summaries separately for quick review.

Key Takeaways

Manual text splitting is slow and error-prone.

Text chunking strategies break text into meaningful parts automatically.

This improves AI understanding and user experience with large texts.

Practice

1. What is the main purpose of text chunking in AI models?

easy

A. To generate new text from scratch

B. To split long text into smaller, manageable pieces

C. To remove stop words from text

D. To translate text into different languages

Solution

1. Step 1: Understand the concept of text chunking

   Text chunking means breaking a long text into smaller parts so it is easier to handle.

2. Step 2: Identify the main goal in AI context

   This helps AI models process and understand large texts better by working on smaller pieces.

3. Final Answer:

   To split long text into smaller, manageable pieces -> Option B

4. Quick Check:

   Text chunking = splitting text [OK]

Hint: Chunking means breaking text into smaller parts [OK]

Common Mistakes:

• Confusing chunking with translation
• Thinking chunking removes words
• Believing chunking generates new text

2. Which of the following is a correct way to create overlapping text chunks in Python?

easy

A. chunks = [text[i:i+chunk_size] for i in range(0, len(text), overlap)]

B. chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]

C. chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size - overlap)]

D. chunks = [text[i:i+chunk_size] for i in range(overlap, len(text), chunk_size)]

Solution

1. Step 1: Understand overlapping chunk logic

   To create overlapping chunks, the step size must be smaller than chunk size by the overlap amount.

2. Step 2: Check the range step in options

   chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size - overlap)] uses chunk_size - overlap as step, correctly creating overlaps.

3. Final Answer:

   chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size - overlap)] -> Option C

4. Quick Check:

   Overlap step = chunk_size - overlap [OK]

Hint: Overlap step = chunk size minus overlap length [OK]

Common Mistakes:

• Using chunk_size as step (no overlap)
• Using overlap as step (too small steps)
• Starting range at overlap instead of zero

3. Given text = 'abcdefghij', chunk_size = 4, and overlap = 2, what is the output of this code?

chunks = [text[i:i+chunk_size] for i in range(0, len(text)-overlap, chunk_size - overlap)]
print(chunks)

medium

A. ['abcd', 'cdef', 'efgh', 'ghij']

B. ['abcd', 'efgh', 'ij']

C. ['abcd', 'bcde', 'cdef', 'defg']

D. ['abcd', 'bcdf', 'cdeg', 'defh']

Solution

1. Step 1: Calculate step size

   Step = chunk_size - overlap = 4 - 2 = 2.

2. Step 2: Generate chunks using step 2

   Chunks are:
   i=0: text[0:4] = 'abcd'
   i=2: text[2:6] = 'cdef'
   i=4: text[4:8] = 'efgh'
   i=6: text[6:10] = 'ghij'

3. Final Answer:

   ['abcd', 'cdef', 'efgh', 'ghij'] -> Option A

4. Quick Check:

   Chunks overlap by 2 chars = ['abcd', 'cdef', 'efgh', 'ghij'] [OK]

Hint: Step = chunk size minus overlap; slice text accordingly [OK]

Common Mistakes:

• Ignoring overlap and stepping by chunk size
• Wrong slicing indices
• Confusing overlap with chunk size

4. This code aims to chunk text with overlap but has a bug:

chunk_size = 5
overlap = 2
chunks = []
for i in range(0, len(text), chunk_size + overlap):
    chunks.append(text[i:i+chunk_size])
print(chunks)

What is the error?

medium

A. Step size should be chunk_size - overlap, not chunk_size + overlap

B. Chunk size should be increased by overlap

C. Overlap should be zero for chunking

D. The loop should start at overlap, not zero

Solution

1. Step 1: Understand step size for overlapping chunks

   To create overlap, step size must be less than chunk size by overlap amount.

2. Step 2: Identify incorrect step in code

   Code uses chunk_size + overlap which skips overlap, causing gaps.

3. Final Answer:

   Step size should be chunk_size - overlap, not chunk_size + overlap -> Option A

4. Quick Check:

   Overlap step = chunk_size - overlap [OK]

Hint: Overlap step = chunk size minus overlap, not plus [OK]

Common Mistakes:

• Adding overlap instead of subtracting
• Setting overlap to zero incorrectly
• Changing loop start index wrongly

5. You have a very long document and want to chunk it for an AI model. You want each chunk to have 100 words and overlap by 20 words to keep context. Which strategy balances chunk size and context best?

hard

A. Use chunk size 80 and step size 100 to create non-overlapping chunks

B. Use chunk size 100 and step size 100 to create overlapping chunks

C. Use chunk size 120 and step size 100 to create overlapping chunks

D. Use chunk size 100 and step size 80 (100 - 20) to create overlapping chunks

Solution

1. Step 1: Define chunk and step sizes for overlap

   Chunk size is 100 words, overlap is 20 words, so step size = 100 - 20 = 80.

2. Step 2: Choose correct step size to maintain overlap

   Step size 80 means each chunk starts 80 words after previous, overlapping 20 words.

3. Final Answer:

   Use chunk size 100 and step size 80 (100 - 20) to create overlapping chunks -> Option D

4. Quick Check:

   Step = chunk size - overlap = 80 [OK]

Hint: Step size = chunk size minus overlap for best context [OK]

Common Mistakes:

• Using step size larger than chunk size
• Setting overlap to zero accidentally
• Confusing chunk size with step size