Are you facing these challenges with AI implementation?
While the introduction of generative AI and RAG chatbots is progressing, many of the following challenges are also frequently seen.
・AI has been introduced but is not used in actual work
・Answers fluctuate every time / cannot be trusted
・Cannot be integrated into operations, so utilization does not take root
・Stopped at PoC and not deployed in production
While AI tools themselves are evolving, there are many cases where they are not used in business as much as expected.
One of the reasons for this is that the structure and expression of the knowledge AI refers to are not suitable for AI utilization.
AI's answer accuracy varies depending on the “knowledge”
AI does not fully understand the meaning of documents like a human does.
It searches and extracts information based on knowledge such as manuals and business documents to generate answers.
Therefore,
・Information is scattered across multiple documents
・Terminology and expressions are not standardized
・Procedures and rules are ambiguous
・The structure makes searching difficult
In such conditions, it becomes difficult for AI to provide accurate and consistent answers.
When utilizing AI, it is important not only to select the AI tools but also to organize the knowledge so that AI can easily understand it.
What is the AI Knowledge Optimization PoC Service
The AI Knowledge Optimization PoC Service is a service that improves existing manuals and business documents into a form that AI can easily understand and verifies the impact on answer accuracy.
We visualize "Can that knowledge be used by AI?" through diagnosis, improvement, and verification.
For example,
・Causes of AI incorrect answers
・Reasons why answer quality is unstable
・Where improvements can enhance accuracy
We organize these points and compare and verify the effects before and after improvement.
Knowledge Improvement and Accuracy Verification in 3 Steps
This service implements the process from identifying issues to improvement and verification in the following three steps.
STEP1 Diagnosis
Analyze existing manuals and business documents,
・Whether AI can easily understand and search them
・Whether the structure is easy for people to understand
From both perspectives, we organize issues that affect AI accuracy.
STEP2 Improvement
We improve the target materials into a form that AI can easily understand.
For example,
・Organizing heading structures
・Unifying terminology and expressions
・Clarifying sentences
・Adjusting the granularity of information
and so on.
STEP3 Accuracy Verification
We compare AI response accuracy before and after improvements to verify the effects of knowledge enhancement.
Additionally, we support comparative verification across multiple environments such as Google NotebookLM and Copilot Notebook.
AI accuracy can improve this much with knowledge enhancement alone
In actual PoCs, improvements in answer quality due to knowledge enhancement have been confirmed.
For example,
・Ability to provide answers based on materials: Approximately 2x improvement
・Ability to correctly extract necessary information: +68% improvement
・Stability of answer quality: +64% improvement
Such improvement effects have been achieved.
Before
・Answers including guesses occur
・Answer content changes every time
・Cannot correctly extract necessary information
After
・Able to provide answers based on documents
・Consistent answers are generated
・Able to systematically and comprehensively extract necessary information
As shown here, even improvements in knowledge alone can lead to significant changes in AI response quality.
Case Studies
Case Study of Manual Organization for AI in the Financial Industry
A certain financial institution carried out internal manual organization in preparation for utilizing a generative AI chatbot.
Before implementation,
・Information is scattered across multiple documents
・Writing rules and terminology are not standardized
・It is difficult to search for necessary information
・Variations and speculative answers occur in AI responses
These were the issues.
Therefore,
・Organizing the manual structure
・Unifying the heading system
・Standardizing terms and expressions
・Organizing information for easy AI reference
These were implemented.
As a result, we confirmed improvements in response accuracy and stabilization of response quality in the generative AI chatbot.
FAQ
We introduce frequently asked questions about the AI Knowledge Optimization PoC Service.
Q1. What is the AI Knowledge Optimization PoC Service?
This service improves existing manuals and business documents for AI use and compares and verifies AI response accuracy before and after the improvements.
You can confirm through the PoC how AI response quality changes as a result of knowledge improvement.
Q2. Can I consult before introducing RAG?
Yes, you can.
You can consult with us even at the stage before introducing RAG, such as when you want to check if your current knowledge is suitable for AI utilization or when you are unsure which AI environment is appropriate.
Q3. What types of documents are applicable?
Business manuals, FAQs, procedure documents, regulations, internal knowledge, and other documents you want AI to reference are applicable.
In particular, we support the organization and improvement of business materials used in RAG chatbots and AI agents.
Q4. In which AI environments can testing be conducted?
We support testing in multiple environments, including Google NotebookLM, Copilot Notebook, and RAG environments utilizing domestic LLMs.
It is also possible to perform comparative testing according to the use case and purpose.
Q5. Is support available after the PoC?
Yes, it is.
Based on the PoC results, we also provide consultations for the next phase, including support for RAG utilization, AI operation design, and AI-oriented knowledge organization.
Contact Us
Human Science provides various support services that underpin AI utilization, such as RAG implementation, AI-oriented knowledge organization, and manual improvement.
"Feeling issues with answer accuracy after AI implementation"
"Want to confirm if in-house knowledge is suitable for AI utilization"
If this applies to you, please feel free to contact us.
In the free downloadable materials, we also introduce cases of AI knowledge improvement and examples of accuracy verification through PoC.
Related
>>Development of AI Agents for Manual Standardization
>> RAG Implementation Support
