Understanding Knowledge Management

Knowledge Management (KM) refers to the systematic process of creating, organizing, sharing, using, and managing information and knowledge within an organization. It includes strategies and practices to identify, generate, map, and distribute knowledge – for reuse, transparency, and learning.

The overarching goal of KM is to increase the efficiency of an organization by avoiding duplication of work and providing employees with quick access to the intellectual capital and collective learning of the company. In the context of AI, large language models (LLMs) are often used to make knowledge accessible through chatbots or other forms of interaction.

Introduction to AutoGen

In the context of AI technologies, AutoGen is usually understood as an automated system that generates computer code or algorithms based on predefined criteria. The technology plays an important role in programming and software development by automating repetitive tasks, generating code from high-level descriptions, and solving complex algorithmic problems.

AutoGen is particularly useful in environments where rapid development and deployment of software solutions are critical.

Limitations of Language Models in Mathematics

One of the biggest limitations of large language models (LLMs) and knowledge-based chatbots is their lack of native ability to perform complex mathematical calculations. LLMs process and generate text based on patterns from their training; they do not "understand" mathematics in the classical sense.

If a calculation or method was not included in the training material, erroneous results may occur. Despite improvements in newer versions of LLMs with basic arithmetic and logical reasoning, they are not designed to consistently solve advanced mathematical or algorithmic tasks independently.

How AutoGen Enhances AI Capabilities

This is where AutoGen becomes valuable. By integrating AutoGen or similar automated coding technologies into chatbots or knowledge management systems, the mathematical limitations of LLMs can be overcome.

AutoGen allows AI systems to communicate with each other, generate task-specific code, execute it, and then translate the mathematical result into natural language by the LLM.

This synergy enables the LLM to not only provide text knowledge but also to accurately answer mathematical queries through automated computation.

Scavenger's New AI Tool: Rethinking Data Interaction

Scavenger's latest AI tool exemplifies the connection between knowledge management and advanced computational power. Users can upload their own data to build a robust knowledge management system.

Thanks to AutoGen integration, not only is interactive querying of this data possible through conversation, but also solving mathematical and predictive questions.

For example: Instead of just querying the 2023 financial statements, results for 2024 can now be forecasted – by combining historical analyses with predictive algorithms.

This tool not only enhances data accessibility but also expands the functional reach of AI in business-relevant decisions.

Book a demo to see how Scavenger AI works in your organization.