Have you ever searched for something online and had to dig through pages of results to find what you actually needed? Or asked an AI assistant a question, only to get an answer that sounded right but was completely wrong? These problems happen because search engines and AI models struggle with retrieving and structuring information in a way that makes sense.

One of the key factors in making search more effective is how information is broken down before it’s retrieved. This is where chunking comes in—a technique that has been used for decades to organize text into meaningful parts. While chunking has always been important, it’s now playing a critical role in AI-powered search, particularly in Retrieval-Augmented Generation (RAG).

RAG is an advanced method that combines traditional search with AI-generated responses. Instead of just listing documents like a search engine or making up answers based on past training like a chatbot, RAG retrieves real-time information from trusted sources and then uses AI to generate a precise, context-aware response. But for this to work, the system needs well-structured chunks of information. Without them, even the smartest AI struggles to deliver useful answers.

What is Chunking?

Chunking is exactly what it sounds like- breaking down large documents or texts into smaller, manageable pieces.

When you ask a search engine or AI assistant a question, it doesn't scan entire documents at once. Instead, it looks through these smaller "chunks" to find the most relevant pieces of information to answer your question.

Every time you search for information online, use a company knowledge base, or ask a virtual assistant a question, chunking is working behind the scenes to determine how accurate and helpful your results will be.

Why is Chunking so Important for AI Search?

Chunking allows AI to process large amounts of text by breaking it into smaller sections. Without chunking, search results may include too much irrelevant information or lack enough context to be useful. Because AI cannot process entire documents at once, breaking them into manageable parts ensures that relevant details can be retrieved when needed.

AI models have a limit on how much text they can process at once, known as the context window. If a document is too long, AI cannot analyze it in full. Search systems that use vector embeddings also have similar size restrictions, making it necessary to divide large documents into smaller segments.

For example, an AI model may process only 8,000 tokens at a time, which is roughly a few pages of text. Because legal contracts, technical manuals, or research papers often exceed this limit, important details could be overlooked without chunking. By dividing these documents into meaningful sections, AI ensures that searches remain accurate and complete, even when dealing with large volumes of information.

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Achieving Chunking Balance

To achieve both search relevancy and accuracy, it's important to find the right balance in how information is chunked.

When chunks are too large, they contain too much unrelated content, making searches less precise. For example, if a return policy is buried in a lengthy "Terms and Conditions" document, retrieving the entire file instead of the specific section forces users to sift through unnecessary details. Because AI struggles to isolate relevant information in large chunks, search results may feel vague or unhelpful.

On the other hand, small chunks lack context. A sentence like "The request was approved on Monday." does not provide enough information without the surrounding details. Because AI may retrieve only isolated fragments, search results may feel incomplete or confusing.

As a result, chunking must balance detail and precision. Well-structured chunks allow AI to locate the right information without losing meaning, improving both search accuracy and usability.

How AI "Understands" Chunks: Tokens and Embeddings

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AI processes text in a structured way, converting words into formats it can analyze efficiently. This begins with tokenization, where text is broken down into small units called tokens. These tokens can be individual words, subwords, or even characters, depending on the language and complexity of the text. Tokenization ensures that AI can handle different writing styles, synonyms, and sentence structures without losing meaning.

Once tokenized, these chunks are transformed into vector embeddings—numerical representations that capture meaning beyond exact word matches. This allows AI to recognize similarities between phrases even when different wording is used.

For example, if an employee searches for:
"How many vacation days do I get?"
AI might retrieve a chunk that says:
"Full-time employees receive 20 days of paid leave per year."

Despite the different phrasing, embeddings enable AI to understand that "vacation days" and "paid leave" are contextually related. Because of this, modern search systems can provide precise answers without relying solely on keyword matching.

Different Types of Chunking and Their Role in RAG

Not all chunking methods are the same. The way a document is split impacts how well AI retrieves and processes information. Choosing the right chunking technique can mean the difference between highly relevant responses and fragmented or incomplete results. Here are the most common chunking strategies and their role in improving Retrieval-Augmented Generation (RAG):

1. Fixed-Length Chunking

This method divides the text into equal-sized segments, such as every 200 words or 1,000 characters, without considering the content. It is efficient and easy to implement, making it suitable for structured data like news articles or database entries. However, it often splits sentences or concepts awkwardly, which can weaken retrieval accuracy.

2. Random-Sized Chunking

Chunks vary in size, sometimes even randomly, within a set range. This approach is rarely used alone because it risks cutting off important details unpredictably. However, in diverse datasets—such as a mix of web pages, FAQs, and manuals—it can reduce size-related biases and ensure different levels of detail are captured.

3. Overlapping Chunks (Sliding Window)

Each chunk slightly overlaps with the previous one, ensuring continuity across boundaries. For example, if chunk 1 contains sentences 1-5, chunk 2 will include sentences 5-10. This technique prevents key details from being lost but increases redundancy, requiring more storage and processing power. RAG systems frequently use this approach to maintain context across retrieved segments.

4. Semantic or Context-Aware Chunking

Text is split based on logical sections such as paragraphs, headings, or sentence boundaries. This ensures that each chunk contains a coherent idea, improving retrieval precision. While computationally more demanding, it is highly effective for documents with well-defined structures, such as research papers or legal texts.

5. Adaptive or ML-guided chunking

A machine learning model determines optimal chunk boundaries by analyzing topic shifts, sentence embeddings, or structure. This method is tailored to the dataset, ensuring highly relevant retrieval. While it delivers precise results, it is complex to implement and computationally intensive, making it most valuable for high-stakes applications.

RAG systems do not rely on a single chunking method but combine multiple strategies to optimize retrieval. For example, semantic chunking ensures logically structured segments, while overlapping chunks preserve context across boundaries. In advanced setups, adaptive chunking fine-tunes segmentation based on the dataset. Together, these techniques enable AI-powered search to provide deep, cross-document insights, such as comparing a Q3 sales report with regional trends and customer feedback, leading to more informed decision-making.

Real-World Examples of Chunking in AI-Powered Search

Chunking is a critical component of Retrieval-Augmented Generation (RAG), making AI search more efficient and accurate across industries. By breaking down large documents into meaningful sections, chunking allows AI to pinpoint and retrieve only the most relevant information—saving time and effort. Here’s how chunking transforms professional search experiences:

1. Instant Access to Company Policies

A new employee searching for flexible work policies on a company intranet might traditionally receive a long list of PDFs, emails, and handbooks, requiring manual searching. With RAG-powered enterprise search, the system scans these documents at the chunk level, retrieving only the relevant sections—such as a paragraph from an HR policy PDF and a recent company-wide email update. Instead of reading multiple documents, the employee gets an instant summary with source references for further verification.

2. Faster Insights for Analysts and Researchers

Market researchers and business analysts often need to pull data from multiple reports. Instead of manually opening four quarterly sales reports, a RAG system with chunking retrieves only the relevant financial figures from each report and aggregates them into a concise answer. This eliminates the need for time-consuming document review, allowing professionals to focus on analysis rather than search.

3. Efficient Problem-Solving for IT and Customer Support

Technical teams often search through support tickets, logs, and knowledge bases to find past solutions. A support engineer troubleshooting an issue from last year can ask, “How was issue XYZ resolved?” Instead of skimming through dozens of tickets, the RAG system retrieves only the relevant chunks from logs and documentation, providing a clear and historically accurate solution in seconds.

4. Legal and Contract Review at Scale

Lawyers and contract managers need to quickly find clauses across many contracts. Instead of reading full documents, a RAG-powered system with chunking allows them to ask, “Does any contract contain a clause about data privacy?” The AI scans each document at the clause level, pulling out only the sections that mention data privacy, along with citations—drastically reducing the time spent on legal review.

These examples highlight how chunking enables AI to retrieve precise, contextual information rather than full documents. Research confirms that smarter chunking strategies—such as context-aware and mix-of-granularity chunking—significantly improve retrieval accuracy. By ensuring AI systems extract the right information, chunking allows professionals to work smarter, make faster decisions, and rely on AI search as an expert assistant rather than just a keyword tool.

Akooda’s Approach to RAG and Chunking for Enterprise Search

Akooda’s RAG-powered enterprise search is designed to handle the complexity of business data, pulling insights from emails, reports, documents, and meeting transcripts. To ensure relevant and context-rich results, Akooda applies chunking techniques that align with the structure of enterprise data to help professionals retrieve precise answers wherever they are stored (Slack, G-drive, emails, Jira, Figma, etc..).

Instead of relying solely on fixed-size chunks, Akooda’s system ensures retrieved segments maintain their meaning and relevance. For example, a search for a policy update may surface a snippet from an HR document alongside a relevant company-wide email—allowing users to see both the official rule and its latest interpretation.

Additionally, Akooda’s search results include document context, so users can verify data sources and click through to the original file when needed. This enhances trust and usability, ensuring AI-generated answers are backed by real organizational knowledge.

By leveraging chunking to improve retrieval accuracy, Akooda’s enterprise search streamlines workflows, reduces search time, and delivers precise, actionable insights—helping teams make informed decisions faster.

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