From Retrieval to Reasoning: Designing Self-Extending Knowledge Systems for Enterprise AI
📰 Medium · Machine Learning
Learn how to design self-extending knowledge systems for enterprise AI that evolve beyond information retrieval to enable continuous learning and improvement
Action Steps
- Design a knowledge graph to store and manage organisational knowledge
- Implement a retrieval mechanism to fetch relevant information from the knowledge graph
- Develop a reasoning engine to enable the system to draw inferences and make decisions
- Integrate a feedback loop to allow the system to learn from user interactions and adapt to changing organisational needs
- Test and evaluate the system's performance using metrics such as accuracy, recall, and F1 score
Who Needs to Know This
AI engineers, data scientists, and product managers can benefit from understanding how to design self-extending knowledge systems to improve enterprise AI capabilities
Key Insight
💡 Self-extending knowledge systems can enable enterprise AI to continuously learn and improve by integrating retrieval, reasoning, and feedback mechanisms
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🤖 Next-gen enterprise AI systems will evolve beyond retrieval to enable continuous learning & improvement! 💡
Key Takeaways
Learn how to design self-extending knowledge systems for enterprise AI that evolve beyond information retrieval to enable continuous learning and improvement
Full Article
Most AI systems retrieve information. The next generation of enterprise AI systems will continuously evolve, expand organisational… Continue reading on Medium »
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