AVILEN Launches 'Knowledge Management Training in the Generative AI Era' to Maximize RAG Utilization
AVILEN has begun offering a practical training program to maximize RAG effectiveness by teaching employees how to structure data into 'AI-readable' formats.
📋 Article Processing Timeline
- 📰 Published: April 9, 2026 at 20:00
- 🔍 Collected: April 9, 2026 at 11:31
- 🤖 AI Analyzed: April 20, 2026 at 09:12 (261h 41m after Collected)
AVILEN Inc. (Headquarters: Chuo-ku, Tokyo; Representative Director: Kotaro Takahashi; hereinafter 'AVILEN') has started offering a new program, 'Knowledge Management Training in the Generative AI Era', predicated on the use of generative AI such as RAG (Retrieval-Augmented Generation).
This training is a practical program that supports the transformation from conventional 'human-readable materials' to AI-readable data formats that allow AI to correctly recognize and utilize information. It has already been introduced in advance at a major financial institution to maximize the RAG utilization of internal knowledge, and its extremely high effectiveness in practical business has been confirmed.
## What is 'Knowledge Management Training in the Generative AI Era'?
This training is a program that cultivates human resources who can understand, practice, and operate the knowledge circulation process in the AI era, assuming the use of 'RAG (Retrieval-Augmented Generation)' which allows AI to reference unique internal company knowledge.
Going beyond merely learning how to use tools, participants systematically acquire everything from the basic theory of Knowledge Management (KM) to the processing techniques necessary for the 'ideal state of data' required for AI to answer correctly, over a two-day period.
## Background
Many companies are introducing generative AI and RAG, but they face challenges such as 'response accuracy not improving' and 'not being utilized in the field'. The biggest reason for this is that the accuracy of RAG depends more on the 'state of the referenced data' than on the performance of the models or tools.
Conventional knowledge is managed based on 'human readability', and the majority of it—such as 'diagrams (like tree diagrams) where the whole can be grasped at a glance' or 'documents covering the entire history of events'—makes it difficult for AI to determine the connections or freshness of the information. To make AI a business partner, the key to raising the overall AI utilization level of the organization is for all employees who generate data in their daily work, not just a few managers or specialists, to output with an awareness of a 'format easy for AI to understand (AI-readable)'.
We launched this training to solve these corporate challenges.
## Features of 'Knowledge Management Training in the Generative AI Era'
The greatest feature of this training is that it balances the understanding of the 'core principles' of how RAG works with practical 'AI-readable' data preparation techniques directly linked to real business.
### 1. Understanding the mechanism of RAG and the 'importance of data' from the essence
Rather than simply learning data processing methods, participants learn the internal mechanisms of RAG regarding 'why AI cannot read that data'. By understanding the principles, they become able to define the 'ideal state of data' themselves to derive highly accurate answers without relying on tools or models.
### 2. Data preparation process towards 'AI-readable'
Based on the principle that 'RAG's accuracy is determined by the state of the data', participants acquire specific techniques to modify unstructured data into formats that AI can accurately extract and understand. This is an essential skill in the AI-first era: reconstructing documents previously prioritized for human readability into highly AI-compatible formats.
### 3. Six types of practical work directly linked to departmental operations
In addition to 'exercises on rewriting into AI-readable documents'...
This training is a practical program that supports the transformation from conventional 'human-readable materials' to AI-readable data formats that allow AI to correctly recognize and utilize information. It has already been introduced in advance at a major financial institution to maximize the RAG utilization of internal knowledge, and its extremely high effectiveness in practical business has been confirmed.
## What is 'Knowledge Management Training in the Generative AI Era'?
This training is a program that cultivates human resources who can understand, practice, and operate the knowledge circulation process in the AI era, assuming the use of 'RAG (Retrieval-Augmented Generation)' which allows AI to reference unique internal company knowledge.
Going beyond merely learning how to use tools, participants systematically acquire everything from the basic theory of Knowledge Management (KM) to the processing techniques necessary for the 'ideal state of data' required for AI to answer correctly, over a two-day period.
## Background
Many companies are introducing generative AI and RAG, but they face challenges such as 'response accuracy not improving' and 'not being utilized in the field'. The biggest reason for this is that the accuracy of RAG depends more on the 'state of the referenced data' than on the performance of the models or tools.
Conventional knowledge is managed based on 'human readability', and the majority of it—such as 'diagrams (like tree diagrams) where the whole can be grasped at a glance' or 'documents covering the entire history of events'—makes it difficult for AI to determine the connections or freshness of the information. To make AI a business partner, the key to raising the overall AI utilization level of the organization is for all employees who generate data in their daily work, not just a few managers or specialists, to output with an awareness of a 'format easy for AI to understand (AI-readable)'.
We launched this training to solve these corporate challenges.
## Features of 'Knowledge Management Training in the Generative AI Era'
The greatest feature of this training is that it balances the understanding of the 'core principles' of how RAG works with practical 'AI-readable' data preparation techniques directly linked to real business.
### 1. Understanding the mechanism of RAG and the 'importance of data' from the essence
Rather than simply learning data processing methods, participants learn the internal mechanisms of RAG regarding 'why AI cannot read that data'. By understanding the principles, they become able to define the 'ideal state of data' themselves to derive highly accurate answers without relying on tools or models.
### 2. Data preparation process towards 'AI-readable'
Based on the principle that 'RAG's accuracy is determined by the state of the data', participants acquire specific techniques to modify unstructured data into formats that AI can accurately extract and understand. This is an essential skill in the AI-first era: reconstructing documents previously prioritized for human readability into highly AI-compatible formats.
### 3. Six types of practical work directly linked to departmental operations
In addition to 'exercises on rewriting into AI-readable documents'...