AI Based Knowledge Management
Science & Technology
Introduction
In today’s fast-paced business environment, accessing knowledge efficiently is essential for roles ranging from sales to development and support. The challenge lies in the vast amount of information dispersed across various platforms, including product documentation, websites, manuals, and file shares. Employees often struggle to locate the necessary information required to perform their tasks, leading to inefficiencies and missed opportunities.
The internal project discussed here aims to leverage AI to enhance knowledge accessibility and organization within the company. The underlying premise is straightforward: if employees can find the information they need, they can perform their roles more effectively. This idea extends not only to internal staff but also to clients seeking self-service support.
To address knowledge access challenges, the project explored the use of large language models (LLMs) that allow users to interact in a more natural way. However, there are challenges associated with using LLMs, such as ensuring the accuracy of the responses generated and that they are grounded in the company’s specific knowledge base. The key task involves fine-tuning these models with company-specific data, which requires combining two significant processes: data preparation and response generation.
Data Preparation and Model Training
Data preparation is the crucial first step in ensuring that LLMs can produce accurate and relevant answers. This stage involves integrating data from various sources, cleaning it, and preparing it for use in LLMs. This includes chunking data into manageable pieces, making it easier to process, and creating embeddings—a vector representation of the information that can be efficiently searched.
The project identified the need for an on-premise solution to enhance data security and control. The architecture involves transforming user queries into embeddings, performing a similarity search against a knowledge base, and subsequently appending this information to create an informed prompt for the LLM that generates the user-facing response. This two-step retrieval process ensures that responses are not just generated but are backed by verifiable sources, minimizing the chance of “hallucinations” or inaccurate outputs.
Technologies and Implementation
To implement this system, the team utilized several technologies. They employed Data Vault Builder for data management, ensuring a versatile and scalable way to integrate various data sources. The Langchain framework was instrumental in creating embeddings and managing interactions with LLMs. This architectural choice allowed for the separation of data preparation from model execution, enabling flexibility in testing different LLMs without major overhauls.
The final output of this effort demonstrated promising results, showcasing the ability of the AI to provide relevant, data-driven responses while allowing users to verify the sources of information provided. Users were encouraged to interact with the system by asking questions and confirming the accuracy based on the sources cited in the answers.
Conclusion
Ultimately, the project succeeded in its primary goals of enriching responses with internal knowledge and establishing a scalable and secure infrastructure. Trust in the system was enhanced by providing verifiable sources, and the mechanism for updating and expanding knowledge effectively set the groundwork for future adaptability in the face of evolving business needs.
Keywords
- AI
- Knowledge Management
- Large Language Models (LLMs)
- Data Preparation
- Embeddings
- Data Vault Builder
- Langchain
- On-Premise Solution
- User Trust
- Scalability
FAQ
Q: What was the main goal of the project?
A: The primary objective was to enhance knowledge accessibility and organization within the company using AI technologies.
Q: How does the AI system generate responses?
A: The system transforms user queries into embeddings, performs a similarity search against a prepared knowledge base, and generates human-like responses based on the relevant information retrieved.
Q: What technologies were used in the implementation?
A: The project utilized Data Vault Builder for data management and Langchain for creating embeddings and managing interactions with language models.
Q: Why is it important to verify the sources of AI-generated responses?
A: Verifying sources enhances user trust in the system and ensures that the information provided is accurate and trustworthy.
Q: What challenges did the project face with LLMs?
A: The main challenges included ensuring responses were based on company-specific knowledge and managing the risk of inaccurate or hallucinatory outputs from the LLMs.