Before an AI model like GPT or Gemini can provide smart answers, summarize documents, or generate insights, the input text needs to be prepared carefully. This process, known as text preprocessing, makes sure the AI can process and comprehend the data you supply.
Token limits are a major preprocessing challenge. The most tokens that even sophisticated models can handle in a single request is limited. They will struggle if you give them a whole book or research paper. At this point, chunking(splitting) — the process of dividing lengthy texts into manageable chunks — becomes crucial.
Frameworks like LangChain contain tools like RecursiveCharacterTextSplitter that are made especially for this use. They minimise the loss of meaning while assisting in the division of lengthy texts into digestible sections.
Why Split Text in NLP?
Language models are powerful, but they don’t have infinite memory. For example:
- GPT-4 Turbo supports up to 128,000 tokens (roughly 300 pages of text).
- Other models may handle much less, sometimes only 4,000–8,000 tokens.
When your text exceeds these limits, you need to split it. But if you split text carelessly, you risk:
- Context loss: Important details cut in half between chunks.
- Semantic breaks: Sentences or paragraphs split in the middle, making chunks harder to understand.
The goal of splitting is to preserve semantic coherence — keeping ideas whole and understandable — while staying within token limits.
📱Example: Sending Long Messages on WhatsApp
Imagine you want to send your friend a long story over WhatsApp, but WhatsApp only lets you send 500 characters per message.
- If you paste the whole story in one go, it won’t send (like hitting the AI’s token limit).
- So, you split the story into smaller messages (like chunks).
Now two problems can happen if you split carelessly:
Context loss
- You cut right in the middle of a sentence.
- Message 1: “The hero opened the doo — ”
- Message 2: “ — r and saw a dragon.” → The flow feels broken.
Semantic breaks
- You accidentally separate related parts.
- Message 1 ends with: “The hero raised his sword.”
- Message 2 starts with something completely new: “Meanwhile, in another city…” → Your friend might get confused because the action was cut too sharply.
✅ The solution is to split at natural points (like at the end of a sentence or paragraph) and sometimes repeat a little overlap.
For example, you might copy the last few words of one message into the next:
- Message 1: “…he opened the door and saw a dragon.”
- Message 2: “He saw a dragon breathing fire across the room…”
That way, your friend remembers the scene, and the story feels smooth — just like preserving semantic coherence when chunking text for AI.
What is RecursiveCharacterTextSplitter?
The RecursiveCharacterTextSplitter is a utility in LangChain (and similar NLP libraries) that intelligently splits large text into smaller parts.
Here’s how it works:
- It tries to split text by larger natural boundaries first (paragraphs, sentences).
- If a chunk is still too big, it splits further down to smaller boundaries (words, then characters).
- This recursive process ensures the chunks are manageable but still meaningful. In short, it’s like cutting a long story into chapters, then into scenes, and only as a last resort into lines — making sure each piece still makes sense.
Code Breakdown
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200
)
What does this mean?
-
chunk_size=1000→ Each chunk will be about 1,000 characters long. Think of it as setting the length of each episode in your story. -
chunk_overlap=200→ The end of one chunk overlaps with the next by 200 characters. This ensures continuity.
Real-World Example: Watching a TV Series
Imagine your PDF is a long TV series. If you cut it into episodes without overlap, a scene might end halfway through Episode 1 and continue in Episode 2. Confusing, right?
With overlap, the last 5 minutes of Episode 1 are replayed at the start of Episode 2.
👉 This way, you don’t forget what happened, and the story flows smoothly.
That’s exactly what chunk_overlap=200 does: it repeats part of the previous text to keep context intact.
Practical Use Cases
Chunking text isn’t just academic — it’s used in real projects every day:
- Document Q&A systems: Splitting a PDF or manual into chunks so an LLM can answer specific questions accurately.
- Text summarization: Breaking down large reports into smaller parts before generating summaries.
- Vector databases (FAISS, Pinecone, Chroma): Storing chunk embeddings for efficient semantic search and retrieval.
- Training data preparation: Splitting text before feeding it into custom NLP/LLM training pipelines.
Pros and Cons
✅ Advantages
- Maintains context continuity with overlaps.
- Preserves semantic meaning by splitting at logical points.
- Works well with vector databases and LLMs.
⚠️ Drawbacks
- Increased processing time: More chunks = more computations.
- Higher token cost: Overlaps mean some text is repeated, slightly increasing usage costs.
Conclusion
Thoughtful text splitting is a cornerstone of effective AI text processing. By carefully choosing chunk size and overlap, you make sure your AI has just enough information in each piece without losing the bigger picture.
While RecursiveCharacterTextSplitter is a go-to tool, alternatives exist: sentence-based splitters, semantic chunkers, or token-level splitters. The key is to balance chunk length and context preservation based on your use case.
If you’re building anything from a chatbot to a summarizer, applying these chunking strategies will dramatically improve your results.
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