Why use overlapping chunks instead of non-overlapping?

Overlapping chunks ensure that information at chunk boundaries is seen by at least two adjacent chunks. Without overlap, a sentence that spans two chunks is partially lost, degrading quality for summarization and QA tasks.

What is the best chunk size for RAG?

The optimal chunk size depends on your embedding model and query granularity. 256–512 tokens works well for most semantic search use cases. Larger chunks (1024 tokens) are better for summarization tasks where more context improves coherence.

How should I handle chunk boundaries in my pipeline?

Always split at sentence or paragraph boundaries rather than fixed character counts. After processing each chunk, re-rank or de-duplicate results that appear in overlapping sections before presenting the final answer.

Split a Long Document for AI Processing

When a document is too long to fit in a single LLM context window, it must be split into chunks that are processed separately and then combined. Naive splitting on a fixed character count breaks in the middle of sentences and paragraphs, degrading output quality and causing the model to produce incoherent summaries at chunk boundaries. This example shows a multi-paragraph document and demonstrates how the splitter creates overlapping chunks that always end at sentence boundaries while respecting the target token limit. Overlap between chunks is crucial for tasks like summarization and question answering. Without overlap, information that spans the boundary between two chunks — such as a conclusion that references a premise from the previous paragraph — is invisible to the model processing each chunk independently. A 10–20% overlap ensures each chunk has context from the preceding section. For RAG (Retrieval-Augmented Generation) pipelines, chunk size directly affects retrieval quality. Chunks that are too small lose context; chunks that are too large reduce retrieval precision. The 512-token chunk size shown in this example is a widely used default for semantic search, but the optimal size depends on your embedding model and the granularity of the questions you expect to answer.

Example

The history of artificial intelligence began in the 1950s when researchers first proposed that machines could simulate human reasoning. Alan Turing's famous 1950 paper "Computing Machinery and Intelligence" introduced the concept of the Turing Test, a benchmark for machine intelligence that remains culturally significant today.

Early AI research focused on symbolic reasoning and rule-based systems. Programs like the Logic Theorist and General Problem Solver demonstrated that computers could solve mathematical proofs and puzzles, but they struggled with the open-ended, ambiguous tasks that humans handle effortlessly.

The first AI winter occurred in the mid-1970s when funding dried up after early systems failed to scale beyond toy problems. Researchers had underestimated the complexity of general intelligence and overestimated the capabilities of the hardware of the time.

The rise of machine learning in the 1980s and 1990s shifted the focus from hand-crafted rules to systems that learned patterns from data. Neural networks, support vector machines, and decision trees enabled applications like spam filtering, optical character recognition, and recommendation systems.

The deep learning revolution starting around 2012 transformed the field. Convolutional neural networks achieved human-level performance on image classification benchmarks. Recurrent networks enabled real-time speech recognition. The availability of large datasets and GPU computing made previously intractable models practical.

[ open in AI Prompt Splitter → ]

FAQ

Why use overlapping chunks instead of non-overlapping?: Overlapping chunks ensure that information at chunk boundaries is seen by at least two adjacent chunks. Without overlap, a sentence that spans two chunks is partially lost, degrading quality for summarization and QA tasks.
What is the best chunk size for RAG?: The optimal chunk size depends on your embedding model and query granularity. 256–512 tokens works well for most semantic search use cases. Larger chunks (1024 tokens) are better for summarization tasks where more context improves coherence.
How should I handle chunk boundaries in my pipeline?: Always split at sentence or paragraph boundaries rather than fixed character counts. After processing each chunk, re-rank or de-duplicate results that appear in overlapping sections before presenting the final answer.

Related Examples

Count Tokens in a Paragraph

Token counting is the foundation of every cost and context window calculation wh...

Calculate Context Window Usage for a System Prompt

Every LLM request draws from a fixed context window budget measured in tokens. Y...

Clean and Format a Messy Prompt

Prompts written in word processors or copied from websites often contain invisib...

Format CSV Data for AI Fine-Tuning

Fine-tuning LLMs on custom datasets requires converting raw training data into t...