[2026 Latest] Key Points for Optimizing Chunking to Determine RAG Accuracy

"We introduced an internal AI chatbot, but the search accuracy for internal regulations is so low it's useless." — This issue, which we hear most frequently in our support field, is actually not due to a lack of LLM performance, but rather in how the data is "cut" (chunking). Especially when using internal documents with complex hierarchical structures for RAG (Retrieval-Augmented Generation), simple character-count-based splitting causes a loss of context and leads to hallucinations (plausible lies). In this article, we explain the practical key points of chunking optimization to drastically reduce the burden on IT departments.

1. Why "Character Count Splitting" Fails to Reduce Inquiries to IT Departments

A common trap many companies fall into when building RAG is the method of mechanically splitting data into uniform blocks of 500 or 1,000 characters. However, when analyzing internal regulations in actual support scenarios, we find that specific clauses may span multiple pages or important conditions may be hidden within tables. If you cut data based solely on character count, "prerequisites" and "conclusions" become separated into different chunks, causing the AI to generate answers based on incomplete information.

As a result, users feel that "the AI's answers are inaccurate," leading them to contact IT staff directly via chat or phone—a counterproductive situation that creates more work than before the implementation. Defining splitting rules that maintain the logical structure of documents during the initial construction phase is an absolute requirement for reducing operational load.

2. Implementing "Semantic Chunking" to Dramatically Improve Accuracy

The standard as of 2026 is "Semantic Chunking," which emphasizes meaningful units. This method analyzes the structure of headings (H1/H2/H3) and paragraphs rather than just character sequences, splitting data so that a single topic remains complete. For example, even in the field of In-house EC Construction and Growth Support, cutting vast manuals into semantic units like "Shipping Policy" or "Return Regulations" significantly improves search hit rates.

A common technique in the field is utilizing conversion to Markdown format. By converting PDF or Word files to Markdown once and performing recursive splitting based on heading levels, you can increase the compatibility of structured internal regulations. This allows the AI to accurately grasp even detailed references like "Article 3, Paragraph 2."

3. "Context Restoration" Techniques via Metadata Tagging

When chunks are split, the high-level context of "which document and which section this information belonged to" is inevitably lost. Strategic use of metadata compensates for this. Attribute information such as the original document title, last updated date, and target department is automatically added to each chunk.

Particularly effective is "Summary Injection." By inserting a few lines of the entire document's summary or high-level headings at the beginning of a chunk, you strengthen the relevance calculation during vector searches. In our support work, it is not uncommon to see search accuracy improve by more than 20% just by revising this metadata design. To reduce the burden on IT departments, it is necessary to embed "signposts" within the data so the AI doesn't get lost.

4. RAG Operations in 2026: Continuous Evaluation and Improvement Cycles

Optimizing chunking is not a one-and-done setup. Continuous tuning is essential to keep up with updates to internal regulations and new user question patterns. In our actual support, we build a flow to analyze answer logs that users rated as "not helpful" and identify which chunk caused the incorrect response.

Furthermore, in operations involving complex tasks such as In-house EC Construction and Growth Support, the handling of diagrams and flowcharts becomes important. In 2026, with the spread of multimodal RAG, technology that treats text within images as an integrated part of a chunk has become commonplace. By utilizing these, it is possible to transform a "storm of inquiries" into "self-resolution by AI."