Splitting and chunking text

As programs begin to work with real text—documents, logs, articles, or scraped pages—they quickly encounter text that is too large to treat as a single piece. This lesson exists to introduce the idea that text often needs to be divided into smaller units before it can be processed reliably by downstream systems, including AI components.

Splitting and chunking text is not about understanding meaning yet. It is about preparing text so other parts of a program can work with it at all.

Why large text must be split

Large blocks of text are awkward to process as a whole. They are harder to search, harder to summarize, and often exceed the limits of systems that consume them.

By breaking text into smaller pieces, we make it manageable. Each piece can be examined, transformed, or passed along independently.

This step is a practical necessity in many AI pipelines.

Splitting text into logical units

The simplest form of splitting divides text along existing boundaries. Common examples include lines, sentences, or paragraphs.

In Python, splitting is usually done with string methods. The goal is not perfect linguistic accuracy, but useful separation.

text = "Mercury is small.\nVenus is hot.\nEarth supports life."
lines = text.split("\n")

Each resulting element represents a unit that can be processed on its own.

Chunking text into fixed-size segments

Sometimes text has no convenient boundaries, or those boundaries produce pieces that are still too large. In these cases, text is chunked into fixed-size segments.

Chunking focuses on size rather than meaning. Each chunk is created to stay within known limits.

def chunk_text(text, size):
    return [text[i:i + size] for i in range(0, len(text), size)]

This approach is common when preparing text for systems with strict input limits.

Preserving meaning across boundaries

Splitting and chunking always risk cutting text in awkward places. A sentence may be divided, or a thought may span multiple chunks.

The key idea is awareness. Programs often carry a small amount of overlap or context between chunks to reduce this problem.

Perfect preservation is not required at this stage, but careless splitting can cause downstream confusion.

Using chunked text in downstream systems

Once text is split or chunked, each piece becomes an independent input. Programs can loop over chunks, analyze them, store them, or send them to other systems.

This pattern shows up repeatedly in AI workflows. Text preparation happens once, and many later steps depend on it.

Conclusion

The goal of this lesson was to orient us to splitting and chunking as preparatory steps for working with text at scale. We now recognize when text is too large to handle directly, how it can be divided, and why those divisions matter.

With this understanding, we are ready to treat text as structured input rather than an opaque block of characters.