RAG Pipeline Performance Starts with Document Parsing<\/strong><\/h2>\n\nParsing sits at the very front of the RAG pipeline, but it has the greatest impact on overall retrieval quality. How you structure documents changes chunking, embeddings, and retrieval accuracy in a chain reaction. <\/p>\n\n
Why isn\u2019t text extraction alone enough in a RAG pipeline?<\/strong><\/h3>\n\nExtracting text alone strips away table row and column relationships, heading and body hierarchy, and image context. When table structure is lost, the LLM misinterprets numbers; when hierarchy is lost, chunking goes off track and irrelevant chunks get retrieved. <\/p>\n\n
Converting unstructured documents into structured data goes beyond simply extracting text\u2014it\u2019s about preserving the document\u2019s structure and context as well. Extracting characters and preserving a document\u2019s logical structure are completely different tasks, and that difference determines overall RAG retrieval quality. <\/p>\n\n
<\/div>\n\n![\"Image](\"https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/07\/RAG-프로세스-공통-1024x576.png\")
<\/figure>\n\n<\/div>\n\nFlow connecting chunking, embedding, and retrieval after document parsing in a RAG pipeline<\/strong><\/h3>\n\nThe RAG process runs in the following order: document extraction \u2192 parsing \u2192 chunking \u2192 embedding \u2192 vector DB storage \u2192 retrieval \u2192 LLM answer. <\/p>\n\n
Because each stage is connected in series, it’s difficult to undo quality issues introduced during parsing in later stages. Since chunking strategy also changes depending on the output format (JSON\/HTML), the choice of parsing method affects the design of the entire pipeline. <\/p>\n\n
How Document Parsing Methods Affect Quality Across Every Stage of the RAG Pipeline<\/strong><\/h3>\n\nIf documents are parsed in a way that’s hard for AI to read, chunking boundaries go off track, embedding quality drops, and retrieval results become inaccurate. Even with a good LLM, if it receives poorly parsed data as input, it can’t perform to its full potential. <\/p>\n\n
In the \u201cRevolutionizing Retrieval (2024)\u201d study, a RAG system using a structure-aware parser showed higher answer accuracy than a system based on a general parser, with performance improvements confirmed on about 47%<\/a> of questions. This result shows that improving RAG performance doesn\u2019t have to start only with swapping LLMs or advancing models\u2014significant gains can also come from improving document parsing and structure analysis quality alone. <\/p>\n\n<\/div>\n\n![\"Image](\"https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/07\/블로그-컨텐츠-4-2-수정사항-피드백-반영-완료-3-1024x576.png\")
<\/figure>\n\n<\/div>\n\nCriteria for Choosing a RAG Pipeline Document Parsing Solution<\/strong><\/h2>\n\nWhen choosing a document parsing solution, there are five criteria you must check: supported format coverage, structural extraction accuracy, preservation of hierarchical structure, support for security environments, and pipeline integration method. If even one doesn\u2019t match your deployment environment, it can lead to a full rebuild after implementation. <\/p>\n\n
\u2705 Supported Format Coverage of a Document Parsing Solution: From PDF to HWP\/HWPX and Office<\/strong><\/h3>\n\nFirst, check whether it natively supports all document formats used in your environment. In particular, for HWP\/HWPX, the key is not simple conversion but whether it can read and extract the original structure as-is. <\/p>\n\n
A workaround that converts files to PDF causes formatting, footnotes, and equations to be lost together\u2014and once information is lost, it cannot be recovered in later stages. In Korean public-sector and enterprise environments, HWP\/HWPX documents are widely used, so how accurately the solution can process these formats is the first thing you should verify. <\/p>\n\n
This is where Hancom Data Loader has a clear differentiator. Because it parses based on proprietary HWP SDK core technology, it extracts the original structure of HWP documents as-is without converting to PDF. It also minimizes loss of document layout and formatting information such as track changes, footnotes, equations, and indentation. <\/p>\n\n
\u2705 Structural Extraction Accuracy: Document Layout Analysis and Table\/Image Handling<\/strong><\/h3>\n\nOCR (Optical Character Recognition) alone isn\u2019t enough. To properly process documents that include multi-column layouts, merged cells, and nested tables, DLA (Document Layout Analysis), TSR (Table Structure Recognition), and OCR must work together within a single pipeline. <\/p>\n\n
If these technologies are separated, multi-column layouts and captions get mixed up, and row\/column relationships in merged cells are lost\u2014so the LLM can\u2019t interpret the numbers in tables accurately.<\/p>\n\n
Hancom Data Loader internalizes DLA, TSR, and OCR into a single pipeline without external dependencies.<\/p>\n\n
\u203b The Image Captioning feature is currently in the PoC stage, and the commercial release schedule will be announced later.<\/p>\n\n
<\/div>\n\n![\"Image](\"https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/07\/블로그-콘텐츠-1-2-배경-추가-작업-2-1024x576.png\")
<\/figure>\n\n<\/div>\n\n\u2705 Preserving Document Hierarchy That Determines RAG Pipeline Chunking Quality<\/strong><\/h3>\n\nHeadings, subheadings, and body hierarchy must be preserved during parsing for chunking to happen in meaningful units and for retrieval accuracy to improve. If the hierarchy is lost, conditional logic such as \u201cArticle 3, Paragraph 2\u201d gets split across multiple chunks, and the LLM reads only fragmented content without context. The difference is especially pronounced in Korean official documents and statutes, where indentation-based hierarchy is critical. <\/p>\n\n
Hancom Data Loader’s Level inference engine automatically tags hierarchy levels by analyzing paragraph styles, indentation, and numbering systems. It’s designed to recognize the hierarchical structures unique to Korean official documents and reports, making it a structure specialized for domestic public and administrative document processing. <\/p>\n\n
\u2705 Security Environment and Deployment Method for a Document Parsing Solution<\/strong><\/h3>\n\nFirst, check whether documents can be processed within the internal network without being sent to an external cloud. With a cloud API approach, external transmission of documents is structurally unavoidable, so organizations that require network segmentation\u2014such as public sector, finance, and legal\u2014often cannot adopt it at all. Whether fully on-premises (self-contained) deployment is supported can be the first gate in your evaluation. <\/p>\n\n
Hancom Data Loader is designed to be fully self-contained within an internal network via a Docker REST API, preventing external leakage of documents. The Data Loader API provided as SaaS can be used immediately through API integration without a separate installation process, making it suitable for environments with relatively flexible security requirements or for running pilot tests. <\/p>\n\n
\u2705 Check the RAG Pipeline Integration Method<\/strong><\/h3>\n\nCheck whether parsing outputs are provided in structured formats such as JSON and HTML, and whether REST API and Docker are supported. If the output format doesn\u2019t fit, additional development effort is required for pipeline integration, affecting both schedule and cost. <\/p>\n\n
\ud83d\udca1 Direct parsing of original HWP\/HWPX, a single DLA\u00b7TSR\u00b7OCR pipeline, fully on-premises (self-contained) deployment<\/strong><\/p>\n\nWant to solve all three in one?<\/strong> \ud83d\udc49 Explore Hancom Data Loader<\/a><\/p>\n\n<\/div>\n\nPrecautions Before Adopting a RAG Pipeline Document Parsing Solution<\/strong><\/h2>\n\nYou need to verify format support, on-premises dependency structure, and actual document test results yourself. Features listed in the spec often behave differently in practice, and if you discover this difference after implementation, you may have to start over from scratch. <\/p>\n\n
<\/div>\n\n![\"Image](\"https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/07\/콘텐츠-6-1-수정사항-반영-시안-3-2-1024x576.png\")
<\/figure>\n\n<\/div>\n\nCheck the Gap Between \u201cHWP\/HWPX Parsing Supported\u201d Claims and Actual Processing Methods<\/strong><\/h3>\n\nMost solutions claim HWP\/HWPX support, but in practice they use a workaround that converts files to PDF. During conversion, tables turn into images so table structure recognition (TSR) doesn\u2019t work, and formatting such as footnotes and indentation is lost and can\u2019t be restored later. A \u201cHWP\/HWPX supported\u201d label alone doesn\u2019t tell you whether it\u2019s direct parsing of the original or a conversion workaround\u2014so you must verify the processing method yourself. <\/p>\n\n
Before Adopting an On-Premises Document Parsing Solution, Check for External Dependencies<\/strong><\/h3>\n\nSome solutions claim to be on-premises but internally depend on external OCR APIs or cloud models. In that case, documents leave the internal network and security requirements are no longer met. It\u2019s important to verify the technical architecture directly\u2014whether the OCR, DLA, and TSR engines are all fully internalized. <\/p>\n\n
Problems That Occur When You Adopt a Document Parsing Solution Based Only on Specs<\/strong><\/h3>\n\nEven if a solution supports features as listed in the spec, when you apply it to the actual documents you have, extraction quality may differ from expectations. Tables with many merged cells, multi-column layouts, and PDFs mixed with scanned pages show significantly different results from standard test documents. <\/p>\n\n
You must test with actual document samples you have and allow plenty of time for a pilot period to reduce the risk of having to redo work later. If you build RAG with low parsing quality, you\u2019ll end up having to redo all the downstream stages\u2014chunking, embedding, and retrieval. <\/p>\n\n
<\/div>\n\nStart RAG Pipeline Document Parsing with Hancom Data Loader<\/strong><\/h2>\n\nHancom Data Loader<\/a> is a document parsing solution that converts HWP, HWPX, PDF, OOXML, and more into structured data, eliminating the burden of the document preprocessing stage when building a RAG pipeline.<\/p>\n\nOriginal HWP\/HWPX\/PDF Parsing and a Single Pipeline for Document Structure Analysis<\/strong><\/h3>\n\nHancom Data Loader uses proprietary HWP SDK technology to extract directly from the original without PDF conversion, and processes DLA, TSR, and OCR in a single pipeline. It supports HWP 3.0 through HWP 5.0, preserves various layout elements without loss, and the scope of applied technologies varies by format. <\/p>\n\n
Extracting text alone strips away table row and column relationships, heading and body hierarchy, and image context. When table structure is lost, the LLM misinterprets numbers; when hierarchy is lost, chunking goes off track and irrelevant chunks get retrieved. <\/p>\n\n
Converting unstructured documents into structured data goes beyond simply extracting text\u2014it\u2019s about preserving the document\u2019s structure and context as well. Extracting characters and preserving a document\u2019s logical structure are completely different tasks, and that difference determines overall RAG retrieval quality. <\/p>\n\n
<\/figure>\n\nFlow connecting chunking, embedding, and retrieval after document parsing in a RAG pipeline<\/strong><\/h3>\n\nThe RAG process runs in the following order: document extraction \u2192 parsing \u2192 chunking \u2192 embedding \u2192 vector DB storage \u2192 retrieval \u2192 LLM answer. <\/p>\n\n
Because each stage is connected in series, it’s difficult to undo quality issues introduced during parsing in later stages. Since chunking strategy also changes depending on the output format (JSON\/HTML), the choice of parsing method affects the design of the entire pipeline. <\/p>\n\n
How Document Parsing Methods Affect Quality Across Every Stage of the RAG Pipeline<\/strong><\/h3>\n\nIf documents are parsed in a way that’s hard for AI to read, chunking boundaries go off track, embedding quality drops, and retrieval results become inaccurate. Even with a good LLM, if it receives poorly parsed data as input, it can’t perform to its full potential. <\/p>\n\n
In the \u201cRevolutionizing Retrieval (2024)\u201d study, a RAG system using a structure-aware parser showed higher answer accuracy than a system based on a general parser, with performance improvements confirmed on about 47%<\/a> of questions. This result shows that improving RAG performance doesn\u2019t have to start only with swapping LLMs or advancing models\u2014significant gains can also come from improving document parsing and structure analysis quality alone. <\/p>\n\n<\/div>\n\n<\/figure>\n\n<\/div>\n\nCriteria for Choosing a RAG Pipeline Document Parsing Solution<\/strong><\/h2>\n\nWhen choosing a document parsing solution, there are five criteria you must check: supported format coverage, structural extraction accuracy, preservation of hierarchical structure, support for security environments, and pipeline integration method. If even one doesn\u2019t match your deployment environment, it can lead to a full rebuild after implementation. <\/p>\n\n
\u2705 Supported Format Coverage of a Document Parsing Solution: From PDF to HWP\/HWPX and Office<\/strong><\/h3>\n\nFirst, check whether it natively supports all document formats used in your environment. In particular, for HWP\/HWPX, the key is not simple conversion but whether it can read and extract the original structure as-is. <\/p>\n\n
A workaround that converts files to PDF causes formatting, footnotes, and equations to be lost together\u2014and once information is lost, it cannot be recovered in later stages. In Korean public-sector and enterprise environments, HWP\/HWPX documents are widely used, so how accurately the solution can process these formats is the first thing you should verify. <\/p>\n\n
This is where Hancom Data Loader has a clear differentiator. Because it parses based on proprietary HWP SDK core technology, it extracts the original structure of HWP documents as-is without converting to PDF. It also minimizes loss of document layout and formatting information such as track changes, footnotes, equations, and indentation. <\/p>\n\n
\u2705 Structural Extraction Accuracy: Document Layout Analysis and Table\/Image Handling<\/strong><\/h3>\n\nOCR (Optical Character Recognition) alone isn\u2019t enough. To properly process documents that include multi-column layouts, merged cells, and nested tables, DLA (Document Layout Analysis), TSR (Table Structure Recognition), and OCR must work together within a single pipeline. <\/p>\n\n
If these technologies are separated, multi-column layouts and captions get mixed up, and row\/column relationships in merged cells are lost\u2014so the LLM can\u2019t interpret the numbers in tables accurately.<\/p>\n\n
Hancom Data Loader internalizes DLA, TSR, and OCR into a single pipeline without external dependencies.<\/p>\n\n
\u203b The Image Captioning feature is currently in the PoC stage, and the commercial release schedule will be announced later.<\/p>\n\n
<\/div>\n\n<\/figure>\n\n<\/div>\n\n\u2705 Preserving Document Hierarchy That Determines RAG Pipeline Chunking Quality<\/strong><\/h3>\n\nHeadings, subheadings, and body hierarchy must be preserved during parsing for chunking to happen in meaningful units and for retrieval accuracy to improve. If the hierarchy is lost, conditional logic such as \u201cArticle 3, Paragraph 2\u201d gets split across multiple chunks, and the LLM reads only fragmented content without context. The difference is especially pronounced in Korean official documents and statutes, where indentation-based hierarchy is critical. <\/p>\n\n
Hancom Data Loader’s Level inference engine automatically tags hierarchy levels by analyzing paragraph styles, indentation, and numbering systems. It’s designed to recognize the hierarchical structures unique to Korean official documents and reports, making it a structure specialized for domestic public and administrative document processing. <\/p>\n\n
\u2705 Security Environment and Deployment Method for a Document Parsing Solution<\/strong><\/h3>\n\nFirst, check whether documents can be processed within the internal network without being sent to an external cloud. With a cloud API approach, external transmission of documents is structurally unavoidable, so organizations that require network segmentation\u2014such as public sector, finance, and legal\u2014often cannot adopt it at all. Whether fully on-premises (self-contained) deployment is supported can be the first gate in your evaluation. <\/p>\n\n
Hancom Data Loader is designed to be fully self-contained within an internal network via a Docker REST API, preventing external leakage of documents. The Data Loader API provided as SaaS can be used immediately through API integration without a separate installation process, making it suitable for environments with relatively flexible security requirements or for running pilot tests. <\/p>\n\n
\u2705 Check the RAG Pipeline Integration Method<\/strong><\/h3>\n\nCheck whether parsing outputs are provided in structured formats such as JSON and HTML, and whether REST API and Docker are supported. If the output format doesn\u2019t fit, additional development effort is required for pipeline integration, affecting both schedule and cost. <\/p>\n\n
\ud83d\udca1 Direct parsing of original HWP\/HWPX, a single DLA\u00b7TSR\u00b7OCR pipeline, fully on-premises (self-contained) deployment<\/strong><\/p>\n\nWant to solve all three in one?<\/strong> \ud83d\udc49 Explore Hancom Data Loader<\/a><\/p>\n\n<\/div>\n\nPrecautions Before Adopting a RAG Pipeline Document Parsing Solution<\/strong><\/h2>\n\nYou need to verify format support, on-premises dependency structure, and actual document test results yourself. Features listed in the spec often behave differently in practice, and if you discover this difference after implementation, you may have to start over from scratch. <\/p>\n\n
<\/div>\n\n<\/figure>\n\n<\/div>\n\nCheck the Gap Between \u201cHWP\/HWPX Parsing Supported\u201d Claims and Actual Processing Methods<\/strong><\/h3>\n\nMost solutions claim HWP\/HWPX support, but in practice they use a workaround that converts files to PDF. During conversion, tables turn into images so table structure recognition (TSR) doesn\u2019t work, and formatting such as footnotes and indentation is lost and can\u2019t be restored later. A \u201cHWP\/HWPX supported\u201d label alone doesn\u2019t tell you whether it\u2019s direct parsing of the original or a conversion workaround\u2014so you must verify the processing method yourself. <\/p>\n\n
Before Adopting an On-Premises Document Parsing Solution, Check for External Dependencies<\/strong><\/h3>\n\nSome solutions claim to be on-premises but internally depend on external OCR APIs or cloud models. In that case, documents leave the internal network and security requirements are no longer met. It\u2019s important to verify the technical architecture directly\u2014whether the OCR, DLA, and TSR engines are all fully internalized. <\/p>\n\n
Problems That Occur When You Adopt a Document Parsing Solution Based Only on Specs<\/strong><\/h3>\n\nEven if a solution supports features as listed in the spec, when you apply it to the actual documents you have, extraction quality may differ from expectations. Tables with many merged cells, multi-column layouts, and PDFs mixed with scanned pages show significantly different results from standard test documents. <\/p>\n\n
You must test with actual document samples you have and allow plenty of time for a pilot period to reduce the risk of having to redo work later. If you build RAG with low parsing quality, you\u2019ll end up having to redo all the downstream stages\u2014chunking, embedding, and retrieval. <\/p>\n\n
<\/div>\n\nStart RAG Pipeline Document Parsing with Hancom Data Loader<\/strong><\/h2>\n\nHancom Data Loader<\/a> is a document parsing solution that converts HWP, HWPX, PDF, OOXML, and more into structured data, eliminating the burden of the document preprocessing stage when building a RAG pipeline.<\/p>\n\nOriginal HWP\/HWPX\/PDF Parsing and a Single Pipeline for Document Structure Analysis<\/strong><\/h3>\n\nHancom Data Loader uses proprietary HWP SDK technology to extract directly from the original without PDF conversion, and processes DLA, TSR, and OCR in a single pipeline. It supports HWP 3.0 through HWP 5.0, preserves various layout elements without loss, and the scope of applied technologies varies by format. <\/p>\n\n
If documents are parsed in a way that’s hard for AI to read, chunking boundaries go off track, embedding quality drops, and retrieval results become inaccurate. Even with a good LLM, if it receives poorly parsed data as input, it can’t perform to its full potential. <\/p>\n\n
In the \u201cRevolutionizing Retrieval (2024)\u201d study, a RAG system using a structure-aware parser showed higher answer accuracy than a system based on a general parser, with performance improvements confirmed on about 47%<\/a> of questions. This result shows that improving RAG performance doesn\u2019t have to start only with swapping LLMs or advancing models\u2014significant gains can also come from improving document parsing and structure analysis quality alone. <\/p>\n\n When choosing a document parsing solution, there are five criteria you must check: supported format coverage, structural extraction accuracy, preservation of hierarchical structure, support for security environments, and pipeline integration method. If even one doesn\u2019t match your deployment environment, it can lead to a full rebuild after implementation. <\/p>\n\n First, check whether it natively supports all document formats used in your environment. In particular, for HWP\/HWPX, the key is not simple conversion but whether it can read and extract the original structure as-is. <\/p>\n\n A workaround that converts files to PDF causes formatting, footnotes, and equations to be lost together\u2014and once information is lost, it cannot be recovered in later stages. In Korean public-sector and enterprise environments, HWP\/HWPX documents are widely used, so how accurately the solution can process these formats is the first thing you should verify. <\/p>\n\n This is where Hancom Data Loader has a clear differentiator. Because it parses based on proprietary HWP SDK core technology, it extracts the original structure of HWP documents as-is without converting to PDF. It also minimizes loss of document layout and formatting information such as track changes, footnotes, equations, and indentation. <\/p>\n\n OCR (Optical Character Recognition) alone isn\u2019t enough. To properly process documents that include multi-column layouts, merged cells, and nested tables, DLA (Document Layout Analysis), TSR (Table Structure Recognition), and OCR must work together within a single pipeline. <\/p>\n\n If these technologies are separated, multi-column layouts and captions get mixed up, and row\/column relationships in merged cells are lost\u2014so the LLM can\u2019t interpret the numbers in tables accurately.<\/p>\n\n Hancom Data Loader internalizes DLA, TSR, and OCR into a single pipeline without external dependencies.<\/p>\n\n \u203b The Image Captioning feature is currently in the PoC stage, and the commercial release schedule will be announced later.<\/p>\n\n Headings, subheadings, and body hierarchy must be preserved during parsing for chunking to happen in meaningful units and for retrieval accuracy to improve. If the hierarchy is lost, conditional logic such as \u201cArticle 3, Paragraph 2\u201d gets split across multiple chunks, and the LLM reads only fragmented content without context. The difference is especially pronounced in Korean official documents and statutes, where indentation-based hierarchy is critical. <\/p>\n\n Hancom Data Loader’s Level inference engine automatically tags hierarchy levels by analyzing paragraph styles, indentation, and numbering systems. It’s designed to recognize the hierarchical structures unique to Korean official documents and reports, making it a structure specialized for domestic public and administrative document processing. <\/p>\n\n First, check whether documents can be processed within the internal network without being sent to an external cloud. With a cloud API approach, external transmission of documents is structurally unavoidable, so organizations that require network segmentation\u2014such as public sector, finance, and legal\u2014often cannot adopt it at all. Whether fully on-premises (self-contained) deployment is supported can be the first gate in your evaluation. <\/p>\n\n Hancom Data Loader is designed to be fully self-contained within an internal network via a Docker REST API, preventing external leakage of documents. The Data Loader API provided as SaaS can be used immediately through API integration without a separate installation process, making it suitable for environments with relatively flexible security requirements or for running pilot tests. <\/p>\n\n Check whether parsing outputs are provided in structured formats such as JSON and HTML, and whether REST API and Docker are supported. If the output format doesn\u2019t fit, additional development effort is required for pipeline integration, affecting both schedule and cost. <\/p>\n\n \ud83d\udca1 Direct parsing of original HWP\/HWPX, a single DLA\u00b7TSR\u00b7OCR pipeline, fully on-premises (self-contained) deployment<\/strong><\/p>\n\n Want to solve all three in one?<\/strong> \ud83d\udc49 Explore Hancom Data Loader<\/a><\/p>\n\n You need to verify format support, on-premises dependency structure, and actual document test results yourself. Features listed in the spec often behave differently in practice, and if you discover this difference after implementation, you may have to start over from scratch. <\/p>\n\n Most solutions claim HWP\/HWPX support, but in practice they use a workaround that converts files to PDF. During conversion, tables turn into images so table structure recognition (TSR) doesn\u2019t work, and formatting such as footnotes and indentation is lost and can\u2019t be restored later. A \u201cHWP\/HWPX supported\u201d label alone doesn\u2019t tell you whether it\u2019s direct parsing of the original or a conversion workaround\u2014so you must verify the processing method yourself. <\/p>\n\n Some solutions claim to be on-premises but internally depend on external OCR APIs or cloud models. In that case, documents leave the internal network and security requirements are no longer met. It\u2019s important to verify the technical architecture directly\u2014whether the OCR, DLA, and TSR engines are all fully internalized. <\/p>\n\n Even if a solution supports features as listed in the spec, when you apply it to the actual documents you have, extraction quality may differ from expectations. Tables with many merged cells, multi-column layouts, and PDFs mixed with scanned pages show significantly different results from standard test documents. <\/p>\n\n You must test with actual document samples you have and allow plenty of time for a pilot period to reduce the risk of having to redo work later. If you build RAG with low parsing quality, you\u2019ll end up having to redo all the downstream stages\u2014chunking, embedding, and retrieval. <\/p>\n\n Hancom Data Loader<\/a> is a document parsing solution that converts HWP, HWPX, PDF, OOXML, and more into structured data, eliminating the burden of the document preprocessing stage when building a RAG pipeline.<\/p>\n\n Hancom Data Loader uses proprietary HWP SDK technology to extract directly from the original without PDF conversion, and processes DLA, TSR, and OCR in a single pipeline. It supports HWP 3.0 through HWP 5.0, preserves various layout elements without loss, and the scope of applied technologies varies by format. <\/p>\n\n<\/figure>\n\nCriteria for Choosing a RAG Pipeline Document Parsing Solution<\/strong><\/h2>\n\n
\u2705 Supported Format Coverage of a Document Parsing Solution: From PDF to HWP\/HWPX and Office<\/strong><\/h3>\n\n
\u2705 Structural Extraction Accuracy: Document Layout Analysis and Table\/Image Handling<\/strong><\/h3>\n\n
<\/figure>\n\n\u2705 Preserving Document Hierarchy That Determines RAG Pipeline Chunking Quality<\/strong><\/h3>\n\n
\u2705 Security Environment and Deployment Method for a Document Parsing Solution<\/strong><\/h3>\n\n
\u2705 Check the RAG Pipeline Integration Method<\/strong><\/h3>\n\n
Precautions Before Adopting a RAG Pipeline Document Parsing Solution<\/strong><\/h2>\n\n
<\/figure>\n\nCheck the Gap Between \u201cHWP\/HWPX Parsing Supported\u201d Claims and Actual Processing Methods<\/strong><\/h3>\n\n
Before Adopting an On-Premises Document Parsing Solution, Check for External Dependencies<\/strong><\/h3>\n\n
Problems That Occur When You Adopt a Document Parsing Solution Based Only on Specs<\/strong><\/h3>\n\n
Start RAG Pipeline Document Parsing with Hancom Data Loader<\/strong><\/h2>\n\n
Original HWP\/HWPX\/PDF Parsing and a Single Pipeline for Document Structure Analysis<\/strong><\/h3>\n\n
![\"Document](\"https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/07\/블로그-콘텐츠-9-2-추가-수정-완료-3-2-1024x576.png\")
<\/a><\/figure>\n\n