Why I Started This Project
Recently, I kept seeing recommendations for tutorials on building personal AI knowledge bases. The idea was compelling: imagine uploading your trusted references, professional standards, notes, articles, and research papers into an AI-powered system that could answer questions based on your own curated sources rather than relying solely on a model’s training data.
As someone interested in accounting, auditing, fraud analytics, AI governance, ERP systems, and data science, I immediately saw the potential. A personal knowledge base could become a digital research assistant capable of searching through thousands of pages of COSO publications, Journal of Accountancy articles, ERP documentation, statistics references, and personal notes.
To explore the concept, I decided to build a small prototype using AnythingLLM and a local large language model (LLM).
The Goal
The architecture I wanted to test looked like this:
Google Drive / Documents
↓
AnythingLLM
↓
Vector Database
↓
Local LLM (Qwen)
↓
Answer
The concept is known as Retrieval-Augmented Generation (RAG). Instead of relying only on what the AI model learned during training, the system first searches a collection of documents, retrieves relevant information, and then uses that information to generate an answer.
In theory, this allows the AI to become an expert in any domain for which high-quality reference material is available.
What I Built
I installed:
- Ollama (local AI runtime)
- Qwen 3 language models
- AnythingLLM
- Embedding models for document indexing
To test the system, I uploaded COSO’s publication Achieving Effective Internal Control Over Generative AI into an Accounting & Audit workspace.
The process worked surprisingly well:
- The document was uploaded.
- The text was converted into embeddings.
- The content was stored in a vector database.
- The AI successfully searched the document when answering questions.
From a technical standpoint, the system worked exactly as intended.
What I Learned About Modern AI Knowledge Bases
Before this experiment, terms such as:
- Embeddings
- Vector Databases
- RAG
- Document Chunking
- AI Agents
felt somewhat abstract.
After building the system myself, I finally understood how these pieces fit together.
The most important realization was this:
The valuable asset is not the AI model.
The valuable asset is the knowledge repository.
The AI model is simply the engine that accesses and synthesizes information from that repository.
This distinction is important because AI models will continue to evolve rapidly. The knowledge base, however, remains yours.
Where the Experiment Struggled
The largest issue was performance.
Although the system worked, response times were far slower than expected on my laptop.
Questions that should have taken seconds often required several minutes to generate a response.
In addition, the model frequently displayed extensive reasoning processes before providing an answer. While this may be useful for certain tasks, it significantly increased response times and reduced usability for everyday research.
After some troubleshooting, it became clear that the limitation was not AnythingLLM itself.
The bottleneck was running a local language model on consumer hardware.
While my laptop is perfectly capable for productivity work, data analysis, accounting, and programming, local AI inference introduces a different set of hardware requirements.
This experience highlighted an important reality:
There is a difference between what is technically possible and what is practical.
Why Enterprises Can Make This Work
Large organizations are increasingly building internal AI knowledge systems using a similar architecture:
Policies
Procedures
Training Materials
Audit Reports
ERP Documentation
↓
Knowledge Platform
↓
Enterprise AI
↓
Employee Questions
The difference is that these systems often run on dedicated servers, cloud infrastructure, or specialized hardware.
For an individual user, reproducing the same experience locally may not always be practical.
The Good News: Alternatives Already Exist
After completing the experiment, I realized that many of the benefits I wanted are already available through tools I currently use.
NotebookLM
NotebookLM can ingest large collections of documents and answer questions with source citations.
For document-heavy research, it is remarkably effective.
ChatGPT Projects
Projects allow documents, instructions, and conversations to be organized around specific topics.
For many research and writing tasks, this covers much of the same functionality.
Cloud-Based Knowledge Systems
Platforms such as Claude Projects and cloud-hosted RAG systems offer many of the same capabilities without requiring local hardware resources.
Was the Experiment a Failure?
Not at all.
The project achieved its primary goal:
Learning.
I now understand:
- How RAG works
- How embeddings are created
- How vector databases function
- How AI retrieves information
- Why retrieval quality often matters more than model size
- The trade-offs between local and cloud AI systems
These concepts are increasingly important as AI becomes integrated into accounting, auditing, governance, risk management, compliance, and business operations.
Even though I ultimately removed the software from my laptop, the knowledge gained remains valuable.
Looking Ahead
The most interesting takeaway is not the software itself but the broader direction of knowledge management.
Imagine a future system containing:
- COSO publications
- IIA standards
- Journal of Accountancy articles
- ERP documentation
- Fraud analytics resources
- AI governance frameworks
- Personal notes
- Research papers
Instead of searching through folders, websites, and PDFs, you simply ask a question.
The system searches everything and provides a synthesized answer grounded in trusted sources.
That future already exists in many forms today, and it will likely become increasingly common over the next decade.
My local experiment may not have become a permanent tool, but it provided a valuable glimpse into what the future of professional knowledge management could look like.
And for that reason alone, the project was worth doing.
— Linden Lake
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