Text splitters in Prompt Engineering / GenAI - Deep Dive

Overview - Text splitters

What is it?

Text splitters are tools or methods that break long pieces of text into smaller, manageable parts. These parts can be sentences, paragraphs, or chunks of a certain size. This helps computers understand, process, or analyze text more easily. Splitting text is often the first step in many language-related tasks.

Why it matters

Without text splitters, computers would struggle to handle large texts all at once, leading to slow processing and poor understanding. Imagine trying to read a whole book without chapters or paragraphs—it would be confusing and tiring. Text splitters make it easier to organize and analyze text, improving tasks like search, summarization, and translation.

Where it fits

Before learning about text splitters, you should understand basic text data and how computers read text. After mastering text splitters, you can explore text embedding, natural language processing pipelines, and building AI models that work with text chunks.

Mental Model

Core Idea

Text splitters break big text into smaller pieces so computers can handle and understand it better.

Think of it like...

It's like cutting a large pizza into slices so you can eat it easily instead of trying to eat the whole pizza at once.

┌───────────────┐
│   Large Text  │
└──────┬────────┘
       │ Split into
       ▼
┌──────┬───────┬───────┐
│Chunk1│Chunk2 │Chunk3 │
└──────┴───────┴───────┘

Build-Up - 7 Steps

1

Foundation

What is Text Splitting?

Concept: Introducing the basic idea of dividing text into smaller parts.

Text splitting means taking a long piece of writing and cutting it into smaller pieces. These pieces can be sentences, paragraphs, or fixed-size chunks. This helps computers process text step-by-step instead of all at once.

Result

You get smaller text pieces that are easier to handle.

Understanding that breaking text down makes it easier for machines to work with language.

2

Foundation

Common Text Splitter Types

Concept: Different ways to split text based on natural boundaries or size.

There are many ways to split text: by sentences (using punctuation), by paragraphs (using line breaks), or by fixed sizes (like every 500 characters). Each method suits different tasks.

Result

You can choose how to split text depending on your goal.

Knowing different splitting methods helps pick the right one for your task.

3

Intermediate

Handling Overlaps in Splitting

🤔Before reading on: Do you think overlapping chunks help or confuse text processing? Commit to your answer.

Concept: Introducing overlapping chunks to keep context between pieces.

Sometimes, chunks overlap by a few words or sentences to keep context. For example, chunk 1 ends with some words that chunk 2 starts with. This helps models understand connections between chunks.

Result

Chunks share some text, improving understanding across splits.

Knowing overlaps preserve meaning across chunks prevents losing important context.

4

Intermediate

Splitting by Tokens vs Characters

🤔Before reading on: Which do you think is better for AI models, splitting by tokens or characters? Commit to your answer.

Concept: Explaining the difference between splitting by characters and by tokens (words or subwords).

Characters are single letters or symbols, while tokens are meaningful units like words or parts of words. Splitting by tokens aligns better with how AI models read text, but splitting by characters is simpler.

Result

Token-based splitting matches AI model inputs better than character-based splitting.

Understanding token splitting improves compatibility with language models.

5

Intermediate

Using Text Splitters in Pipelines

Concept: How text splitters fit into larger AI workflows.

Text splitters are often the first step in pipelines that create embeddings, summaries, or answers. They prepare text so models can process it chunk by chunk, making large texts manageable.

Result

Text is ready for AI models to analyze in parts.

Knowing where splitting fits helps design efficient AI workflows.

6

Advanced

Balancing Chunk Size and Context

🤔Before reading on: Is bigger chunk size always better for understanding? Commit to your answer.

Concept: Choosing chunk size affects how much context is kept and how fast processing is.

Large chunks keep more context but require more memory and time. Small chunks are faster but may lose important connections. Finding the right size balances understanding and efficiency.

Result

Optimized chunk size improves model performance and speed.

Knowing this balance helps avoid slow or poor-quality AI results.

7

Expert

Adaptive and Semantic Splitting Techniques

🤔Before reading on: Do you think splitting text by meaning is better than fixed rules? Commit to your answer.

Concept: Advanced splitters use meaning or AI to split text where it makes most sense, not just by fixed rules.

Semantic splitters analyze text meaning to create chunks that keep ideas intact. Adaptive splitters change chunk size based on content complexity. These methods improve AI understanding but are more complex to build.

Result

Chunks better represent ideas, improving AI tasks like summarization or Q&A.

Understanding semantic splitting unlocks higher-quality AI text processing.

Under the Hood

Text splitters scan the input text and identify boundaries based on rules or models. Simple splitters use punctuation or whitespace to find sentences or paragraphs. Token-based splitters use language tokenizers that convert text into tokens matching AI model vocabularies. Advanced splitters may use embeddings or AI models to detect semantic boundaries. Overlapping chunks are created by repeating some tokens between chunks to preserve context.

Why designed this way?

Text is naturally continuous and unstructured, so splitting helps impose structure for machines. Early splitters used simple rules for speed and simplicity. As AI models grew more complex, token-based and semantic splitting became necessary to align with model inputs and preserve meaning. Overlaps were introduced to avoid losing context between chunks, a problem in earlier methods.

Input Text
   │
   ▼
┌───────────────┐
│ Rule-based or  │
│ Tokenizer     │
└──────┬────────┘
       │
       ▼
┌───────────────┐
│ Split Points  │
│ (punctuation, │
│ tokens, AI)   │
└──────┬────────┘
       │
       ▼
┌───────────────┐
│ Chunks with   │
│ optional      │
│ overlaps      │
└───────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Does splitting text always improve AI model results? Commit yes or no.

Common Belief: Splitting text always makes AI models perform better.

Reality: Splitting can sometimes remove important context or create too many chunks, confusing models.

Why it matters: Blindly splitting text can reduce accuracy or increase processing time unnecessarily.

Quick: Is splitting by characters the same as splitting by tokens? Commit yes or no.

Common Belief: Splitting by characters and tokens is the same for AI models.

Reality: Tokens represent meaningful units, while characters are raw letters; models understand tokens better.

Why it matters: Using character splits can misalign with model inputs, hurting performance.

Quick: Do overlapping chunks always add value? Commit yes or no.

Common Belief: Overlapping chunks are always helpful and never cause problems.

Reality: Too much overlap can cause redundant processing and slow down systems.

Why it matters: Excessive overlap wastes resources and can confuse downstream tasks.

Quick: Is semantic splitting easy to implement? Commit yes or no.

Common Belief: Semantic splitting is simple and always better than rule-based splitting.

Reality: Semantic splitting requires complex models and more computation, not always practical.

Why it matters: Choosing complex splitting without need can overcomplicate systems.

Expert Zone

1

Some AI models have maximum token limits, so chunk size must respect these limits to avoid errors.

2

Overlapping chunks require careful tuning; too little overlap loses context, too much wastes compute.

3

Semantic splitting can be combined with rule-based splitting for hybrid approaches balancing speed and quality.

When NOT to use

Text splitters are not needed when working with very short texts or when models can handle entire documents at once. Alternatives include using models designed for long text inputs or hierarchical models that process text at multiple levels.

Production Patterns

In production, text splitters are often combined with caching to avoid repeated splitting. They are tuned for chunk size based on model limits and task needs. Overlaps are carefully set to balance context and efficiency. Semantic splitting is used in high-value applications like legal or medical document analysis.

Connections

Tokenization

Text splitting often uses tokenization as a base step to divide text into meaningful units.

Understanding tokenization helps grasp how text splitters align chunks with AI model inputs.

Data Chunking in Distributed Systems

Both split large data into smaller parts for easier processing and parallelism.

Knowing data chunking in systems helps understand why splitting text improves efficiency and scalability.

Cognitive Chunking in Psychology

Both involve breaking information into smaller units to improve understanding and memory.

Recognizing this connection shows how human and machine processing share similar strategies.

Common Pitfalls

#1 Splitting text without considering model token limits.

Wrong approach:

chunks = text_splitter.split(text) # no size limit check

Correct approach:

chunks = text_splitter.split(text, max_chunk_size=model_token_limit)

Root cause:

Ignoring model input size constraints leads to errors or truncation.

#2 Using character-based splitting for token-based models.

Wrong approach:

chunks = split_by_characters(text, size=500)

Correct approach:

chunks = split_by_tokens(text, size=500)

Root cause:

Mismatch between splitting units and model input units causes poor alignment.

#3 Creating chunks with no overlap in context-sensitive tasks.

Wrong approach:

chunks = split_text(text, overlap=0)

Correct approach:

chunks = split_text(text, overlap=50) # overlap 50 tokens

Root cause:

Lack of overlap loses context between chunks, reducing model understanding.

Key Takeaways

Text splitters break large text into smaller parts to help AI models process and understand language better.

Choosing how to split—by sentences, tokens, or fixed size—depends on the task and model requirements.

Overlapping chunks preserve context but must be balanced to avoid inefficiency.

Advanced splitters use semantic meaning to create smarter chunks, improving AI results.

Understanding text splitting is essential for building effective and efficient language AI systems.