Text chunking strategies in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is text chunking in natural language processing?

Text chunking is the process of dividing text into smaller, meaningful pieces called chunks, such as phrases or sentences, to make it easier for machines to understand and analyze.

beginner

Name two common strategies used for text chunking.

Two common strategies are:
1. Fixed-size chunking: splitting text into equal-sized parts.
2. Semantic chunking: splitting text based on meaning, like sentences or phrases.

intermediate

Why is semantic chunking often better than fixed-size chunking?

Semantic chunking respects the meaning and structure of text, like sentences or paragraphs, which helps models understand context better than arbitrary fixed-size chunks.

intermediate

What is a challenge when using fixed-size chunking?

Fixed-size chunking can split sentences or ideas in the middle, causing loss of meaning and making it harder for models to understand the text properly.

advanced

How can overlapping chunks improve text chunking?

Overlapping chunks include some shared text between chunks, which helps preserve context across chunks and reduces information loss at chunk boundaries.

What does text chunking help with in machine learning?

A Generating images from text

B Translating text into another language

C Breaking text into smaller, meaningful parts

D Encrypting text data

Text chunking breaks text into smaller parts to help machines understand and analyze it better.

Which chunking strategy respects sentence boundaries?

A Semantic chunking

B Fixed-size chunking

C Random chunking

D No chunking

Semantic chunking splits text based on meaning, like sentences, preserving natural boundaries.

What is a downside of fixed-size chunking?

A It can split sentences in the middle

B It always preserves sentence meaning

C It requires complex algorithms

D It only works for images

Fixed-size chunking may cut sentences arbitrarily, losing meaning.

Why use overlapping chunks?

A To reduce chunk size

B To preserve context across chunks

C To speed up processing

D To remove stop words

Overlapping chunks share text to keep context between chunks.

Which is NOT a text chunking strategy?

A Semantic chunking

B Fixed-size chunking

C Random chunking

D Image chunking

Image chunking is unrelated to text chunking.

Explain what text chunking is and why it is useful in natural language processing.

Think about how breaking text into parts helps computers.

Describe the difference between fixed-size chunking and semantic chunking, including one advantage and one disadvantage of each.

Consider how chunks are created and how meaning is preserved.

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