• Written by Azhar Aliyar, Director

Chunking is the process of splitting documents into smaller, manageable pieces, known as chunks, to facilitate efficient retrieval and analysis of information, which in practice often results in incomplete or inaccurate answers due to the complexity of the documents being chunked. What is chunking, in this context, is a mechanism that allows systems like AsscherAi to process and understand large volumes of data by breaking them down into more digestible parts. This process is crucial in determining the quality of answers provided by the system, as it directly affects how the information is retrieved and presented to the user.

Definition and distinction

Chunking is often confused with tagging, which is the process of assigning keywords or labels to specific parts of a document to facilitate search and retrieval. However, chunking is a more nuanced process that involves breaking down documents into smaller, contextual pieces that can be analyzed and understood independently. This distinction is important, as it highlights the unique role that chunking plays in information retrieval and analysis. A shift supervisor checking the maintenance log, for instance, relies on the chunking process to quickly identify and retrieve relevant information about equipment maintenance schedules and procedures.

The difference between chunking and tagging is not just semantic; it has significant implications for how information is retrieved and presented to the user. Tagging provides a way to quickly identify and locate specific pieces of information, but it does not provide the same level of contextual understanding that chunking provides. By breaking down documents into smaller, more manageable pieces, chunking enables systems like AsscherAi to provide more accurate and relevant answers to user queries.

Applications in a large organisation

In a large organisation, chunking is particularly useful for processing and analyzing large volumes of data, such as purchase orders, invoices, and customer records. By breaking down these documents into smaller chunks, systems like AsscherAi can quickly identify and retrieve relevant information, such as the status of a specific purchase order or the details of a customer's account. This enables employees, such as category managers, to quickly compare suppliers and make informed decisions about procurement and inventory management.

The purchase order table, for example, is a critical component of any organisation's procurement process, and chunking plays a key role in facilitating the efficient retrieval and analysis of this information. By breaking down the purchase order table into smaller chunks, systems like AsscherAi can quickly identify and retrieve relevant information, such as the status of a specific purchase order or the details of a supplier's contract.

Limitations and misconceptions

Most people get wrong the idea that chunking is a simple process that can be applied universally to all types of documents and data. However, this is not the case, as chunking requires a deep understanding of the context and structure of the documents being processed. A category manager comparing two suppliers, for instance, relies on the chunking process to quickly identify and retrieve relevant information about each supplier's contract and performance history.

Furthermore, chunking is not a one-size-fits-all solution, and its effectiveness depends on the specific requirements and constraints of the organisation. For instance, chunking may not be suitable for documents that require a high level of semantic understanding, such as legal contracts or technical specifications. In such cases, more advanced natural language processing techniques may be required to ensure accurate and relevant answers.

What chunking does not do

Chunking does not provide a magic solution for information retrieval and analysis, and it is not a replacement for proper data management and organisation. While chunking can facilitate the efficient retrieval and analysis of information, it is only effective when applied to well-structured and well-organised data. For instance, if the purchase order table is not properly maintained and updated, chunking will not be able to provide accurate and relevant answers to user queries.

In addition, chunking is not a substitute for human judgment and expertise, and it should be used in conjunction with other techniques and tools to ensure accurate and relevant answers. If you are evaluating AsscherAi for your organisation, you can learn more about how chunking works and how it can be applied to your specific use case. You can also contact us to discuss your specific requirements and constraints.

When chunking is the wrong choice

Chunking is not suitable for all types of documents and data, and it may not be the best approach for organisations with complex or nuanced information retrieval requirements. In such cases, more advanced techniques, such as natural language processing or machine learning, may be required to ensure accurate and relevant answers. For instance, if an organisation requires a high level of semantic understanding, such as in the case of legal contracts or technical specifications, chunking may not be sufficient to provide accurate and relevant answers.

In addition, chunking may not be suitable for organisations with limited resources or expertise, as it requires a significant amount of time and effort to implement and maintain. In such cases, more straightforward approaches, such as tagging or indexing, may be more effective and efficient. Ultimately, the decision to use chunking depends on the specific requirements and constraints of the organisation, and it should be carefully evaluated and considered before implementation.