RAG Technology: Optimizing the knowledge base to solve the problem of AI not answering questions correctly

As AI big models sweep the world, Retrieval-Augmented Generation (RAG), as a technology that integrates retrieval and generation, is becoming a core tool for enterprises and developers to enhance AI capabilities. However, many users find that when using RAG, AI's answers are often "irrelevant" or even "irrelevant". The reason is often due to improper document processing . This article will deeply analyze the principles and current pain points of RAG, and focus on sharing how to maximize the potential of RAG by optimizing document processing (such as unifying document formats). At the same time, a RAG architecture diagram is attached to help intuitively understand its working mechanism.

What is RAG? From the principle

RAG is a hybrid technology that combines information retrieval and generative models , aiming to improve the accuracy and timeliness of AI answers. Its core idea is to combine the language generation capabilities of a large model with an external knowledge base for real-time retrieval. Compared with traditional language models, RAG can provide more accurate and updated answers by dynamically querying the knowledge base.

The RAG workflow can be divided into three steps:

Retrieval : Extract relevant documents or snippets from the knowledge base based on user queries.

Context integration : The retrieved information is combined with the query context and input into the generative model.

Generate : The model generates natural and accurate responses based on the integrated information.

In theory, RAG can significantly reduce the "hallucination" of large models (generating erroneous or irrelevant information). However, in actual applications, many users find that the quality of RAG's answers is not stable, and the problem often points to a key link - document processing .

Pain point: Improper document processing, AI "answers irrelevant questions"

The core advantage of RAG is to retrieve high-quality information from the knowledge base, but if the documents in the knowledge base are not properly processed, the quality of AI's answers will be greatly reduced. The following are common pain points:

• Messy document formats : The knowledge base may contain multiple formats such as PDF, Word, web pages, Markdown, etc., with inconsistent structures, making it difficult to extract information during retrieval.

• Poor content quality : Documents may contain redundant, outdated, or low-quality content, interfering with retrieval accuracy.

• Unclear semantics : The document lacks clear titles, paragraph divisions, or keyword annotations, making it difficult for AI to understand the relevance of the content to the query.

• Data silos : Internal documents of an enterprise are scattered in different systems and lack integration, so RAG cannot conduct comprehensive retrieval.

These problems directly lead to RAG "missing the point" when answering, or even citing wrong or irrelevant information. For example, when a user asks "the company's 2025 strategic plan", AI may return an outdated 2023 plan, or simply output irrelevant meeting minutes. This not only affects the user experience, but may also reduce the company's trust in AI.

Optimizing document processing: practical methods to make RAG more accurate

Documentation is key to realizing the full potential of RAG. Here are some professional and practical optimization methods, focusing on unifying document formats and improving content quality:

1. Unify document formats to reduce retrieval difficulty

• Standardized formats : Convert documents in the knowledge base into structured formats such as Markdown, JSON, or plain text. These formats are easy for AI to parse and support clear titles, paragraphs, and metadata annotations. For example, Markdown's hierarchical headers (#, ##) can help AI quickly locate content.

• Standardized naming : Set unified naming rules for documents and paragraphs, such as "[department]-[year]-[subject].md", to facilitate retrieval and management.

• Metadata enhancement : Add metadata (such as keywords, creation date, and applicable scenarios) to each document to help RAG accurately match queries. For example, a technical report can be marked with "Keywords: cloud computing, AI; Applicable: technology research and development".

  
  

2. Refine the content and improve semantic clarity

• Segmentation and summary : Split long documents into small segments, attach a short summary to each segment to clarify the topic. RAG can locate relevant segments more quickly during retrieval. For example, a 100-page annual report can be split into chapters, with a sentence "This chapter introduces the 2025 financial targets" at the beginning of each chapter.

• Redundancy removal and updating : Regularly clean up outdated or duplicate content to ensure that the information in the knowledge base is up to date. For example, delete the 2023 policy document and replace it with the 2025 version.

• Semantic optimization : Use clear and concise language to avoid ambiguity. If necessary, introduce keyword indexing or synonym mapping (such as mapping "environmental protection policy" to "green development") to improve search coverage.

  
  

3. Build a structured knowledge base

• Hierarchical organization : Documents are organized by subject, department, or time to form a tree structure. For example, a corporate knowledge base can be divided into modules such as "strategic planning", "technical documents", and "market analysis".

• Embedded vector indexing : Use embedding models (such as GTE, General Text Embeddings) to generate semantic vectors for documents and store them in vector databases (such as Faiss, Pinecone). The GTE model, with its efficient semantic representation capabilities, can capture the deep semantics of documents, significantly improve the semantic retrieval capabilities of RAG, and reduce the limitations of traditional "keyword matching". In addition, combining the Rerank model to reorder the search results can further optimize the relevance and ensure that the most matching documents are used first.

• Cross-system integration : Through API or ETL tools, documents scattered in different systems (such as ERP, CRM) are integrated into a unified knowledge base to ensure that RAG can be fully searched.

  
  

4. Continuous monitoring and feedback

• Retrieval quality assessment : Regularly check the RAG search results to analyze whether the correct documents are hit. If deviations are found, adjust the metadata or content structure of the document.

• User feedback loop : Collect user feedback on the quality of answers, identify the root causes of problems (such as missing documents or unclear annotations), and optimize the knowledge base.

• Automated cleaning : Deploy scripts or tools to automatically detect formatting errors, duplicate content, or outdated information in documents, reducing the burden of manual maintenance.

  
  

Case: From “irrelevant answers” ​​to “accurate hits”

• Convert all documents to Markdown format and add metadata.

• Reorganized the knowledge base by department and year, removing obsolete documents.

• The GTE model is used to generate a semantic vector index, and the Rerank model is introduced to optimize the sorting of retrieval results and improve the accuracy of semantic retrieval.

  
  

  The following figure shows the effect of using EasyRAG. The above process has been encapsulated to achieve fully automatic operation. The following figure shows the effect:

At the same time, the DeepSeek1.5b model will be automatically downloaded to summarize the retrieved content and automatically answer the questions.

Future: Deep integration of RAG and document processing

With the iteration of RAG technology, document processing will become more intelligent. Future knowledge bases may support automatic semantic annotation , multimodal content integration (such as images, tables, videos), and real-time incremental updates to further improve the quality of RAG's answers. At the same time, combined with privacy protection technologies (such as federated learning), RAG can provide accurate answers while protecting sensitive data.

Last words

As the "key" for AI to answer questions accurately, the effectiveness of RAG is highly dependent on the quality of document processing. A disorganized knowledge base will only make AI "more trouble than it helps", while structured, high-quality documents will allow RAG to thrive. Whether it is an enterprise or a developer, the practical value of RAG can be significantly improved by unifying document formats, refining content, and building a structured knowledge base.