Create a navigation layer that helps AI find the right source content
Have you implemented a RAG system or internal AI chatbot, only to find that it fails to retrieve the right information, returns irrelevant passages, or provides answers without clear supporting evidence?
These problems do not arise from the AI model alone. Retrieval methods, document structure, chunking, and insufficient metadata can all affect the quality of the results.
Ideally, source documents would be reorganized specifically for AI retrieval. In practice, however, rebuilding an entire document collection can require considerable time and effort.
Mindware Research Institute provides an “LLM Wiki” that serves as a navigation layer between your existing documents and AI systems, helping AI locate the relevant original content.
What is an LLM Wiki?
An LLM Wiki is not a substitute for the original document, nor is it merely a summary used to generate answers. It is a structured guide that tells AI what each document contains, what kinds of questions it may answer, and where the relevant information can be found in the original source.
An LLM Wiki entry may include:
- An overview and purpose of the document
- Main topics and terminology
- Matters covered by the document
- Questions the document may help answer
- Related documents
- Original file name or document ID
- Relevant chapter, page, heading, or section
- Links to the original source
- Update date and version information
Detailed facts, figures, and conditions remain in the original document. The LLM Wiki helps AI identify the appropriate document and locate the relevant passage.
From question to answer
- A user asks a question.
- The AI searches the LLM Wiki for relevant documents.
- The LLM Wiki identifies the original source and the relevant location.
- The AI reads the corresponding passage in the original document.
- The AI generates an answer grounded in the original source and provides citations when appropriate.
Rather than relying only on direct retrieval from fragmented document chunks, this approach allows the AI to identify the document context before accessing the original content. This can make retrieval more consistent and answers easier to trace.
Use it with your existing system
ThinkNavi provides a complete workflow for creating LLM Wikis and answering questions. If you already use a RAG system, internal chatbot, or generative AI service, however, you do not necessarily need to replace it.
LLM Wikis can be exported together with source references and machine-readable metadata, allowing them to be incorporated into an existing retrieval environment.
Typical export content includes:
- LLM Wiki entries
- Human-readable formats such as Markdown
- Machine-readable formats such as JSON
- Original file names, document IDs, and source locations
- Links to the original documents
- Update dates and version information
- Related documents and conceptual classifications
The integration method will depend on the specifications of the system you currently use. Possible options include file import, metadata-based retrieval, and access through an API or MCP connection.
Suitable use cases
An LLM Wiki may be useful when:
- Your RAG system does not consistently retrieve the right documents
- It is difficult to determine which source supports an AI-generated answer
- Documents are long and relevant passages are difficult to locate
- Different types of documents must be searched together
- Rebuilding the entire document collection is impractical
- You want to retain your existing AI platform or chatbot
- You want AI to verify the original source rather than answer from summaries alone
What an LLM Wiki cannot solve by itself
An LLM Wiki cannot supply information that does not exist in the source documents. If the original content is incorrect, outdated, or incomplete, the AI cannot produce a reliable answer from it.
Some RAG problems may also result from retrieval settings, access permissions, system configuration, or the instructions given to the AI. These issues may require adjustments beyond the LLM Wiki itself.
For this reason, it is generally more practical to begin with a small set of documents and representative questions, then evaluate whether the LLM Wiki improves source identification and retrieval.
Start with a small-scale evaluation
You do not need to disclose all your internal documents or business information at the outset. An initial evaluation can be conducted using a limited selection of documents, publicly available materials, or anonymized samples.
The evaluation can examine:
- Which questions currently produce unsatisfactory results
- Whether the required information exists in the source documents
- Whether the LLM Wiki helps identify the correct source
- Whether the exported information can be integrated into the existing system
Based on the results, the document scope, export format, update process, and integration method can then be considered.
If your RAG system or AI chatbot is not delivering the expected results, please contact us with the information you are able to share.