Two Knowledge Hierarchies: Structuring Context for AI Agents and LLMs
📰 Dev.to · Oscar Rieken
Learn how to structure context for AI agents and LLMs using two knowledge hierarchies, enabling more effective interaction and decision-making
Action Steps
- Identify the types of context required by AI agents and LLMs
- Develop a knowledge hierarchy for agent-centric context
- Create a separate hierarchy for human-centric context
- Map the relationships between the two hierarchies
- Implement the hierarchies in your AI system using tools like knowledge graphs or ontologies
Who Needs to Know This
This benefits teams working with AI agents and LLMs, such as AI engineers, data scientists, and software engineers, by providing a framework for organizing context and improving agent performance
Key Insight
💡 Using two distinct knowledge hierarchies can help AI agents and LLMs better understand context and make more informed decisions
Share This
🤖 Structure context for AI agents & LLMs with 2 knowledge hierarchies! 📈 Improve interaction & decision-making
Key Takeaways
Learn how to structure context for AI agents and LLMs using two knowledge hierarchies, enabling more effective interaction and decision-making
Full Article
TestSmith has two distinct audiences that need context about the project: AI agents that work on the...
DeepCamp AI