Concept Enumeration
Prompt
You are an expert at generating Concept Labels for learning dependency graphs for courses.
I am writing a book on how to create intelligent software agents that use both LLMs and knowledge graphs. There is a course-description.md file in the Project Knoweldge
I want you to generate a list of the 200 most important Concept Labels in this course.
Just return a simple ordered list of the in 200 most important concepts in markdown with no other content. Use title case for each Concept Label.
Use abbreviations when the labels get over 30 characters long.
Return the list in the order the concepts should be learned. A concept can only be added if the prerequisite concepts are already listed.
Use the course-description.md file to find the prerequsites and list them as the first concepts in the list.
Response
I'll create a list of the 200 most important concept labels for a course on intelligent software agents with LLMs and knowledge graphs, based on the documentation provided. The list will be in learning order, with prerequisites first.
Intelligent Software Agents Concept Labels
- Python Programming
- Data Types
- Data Structures
- Version Control
- GitHub
- Integrated Development Environment
- Visual Studio Code
- Command Line Interface
- Software Agents
- Agency Levels
- Knowledge Representation
- Tools
- Tool Calling
- Tool Catalogs
- Local Development Environment
- Ollama
- Large Language Models
- DeepSeek R1
- Prompt Engineering
- Basic Tool-Calling Agents
- Graphics Processing Unit
- GPU Optimization
- SmolAgents Library
- Agent Frameworks
- LangChain
- PydanticAI
- ReAct Framework
- Custom Tools
- Tool Documentation
- Code Agents
- Direct Code Generation
- Code Execution
- Public Knowledge
- Private Knowledge
- Knowledge Sources Integration
- Multi-Agent Systems
- Error Handling
- Safety Considerations
- Intelligent Textbook
- Lightweight LLMs
- GPU Memory Requirements
- Token Generation Rate
- Python Docstrings
- Decorators
- Tool Decorator
- Function Parameters
- Parameter Types
- Return Types
- ISO 11179 Definitions
- Controlled Imports
- Sandboxed Environment
- Code Security
- LLM Integration
- Agent Architecture
- Decision-Making Agents
- Function-Calling Agents
- Iterative Agents
- Workflow Agents
- Code-Generating Agents
- Agentic Execution
- LLM Agent Control
- Agency Spectrum
- Knowledge Graphs
- Ontologies
- Semantic Networks
- Frames
- Rules-Based Systems
- Logic-Based Systems
- Information Organization
- Reasoning Mechanisms
- Semantic Grounding
- Procedural Code
- Declarative Code
- Agentic Code
- JSON Formatting
- LLM Response Parsing
- Python Code Snippets
- Matrix Operations
- Parallel Processing
- Model Inference
- Local LLM Deployment
- FinalAnswerTool
- Reasoning Trace
- Interleaved Reasoning and Action
- Retrieval Augmented Generation
- Knowledge Representation Trade-offs
- Tool API Design
- Action Definition
- Agntcy Standards
- Model Context Protocol
- Tool Name Conventions
- Business Process Modeling Notation
- Goal-Driven Process
- Software Development Life Cycle
- Computational Entities
- Autonomous Systems
- User Goal Representation
- Domain Knowledge
- Rule-Based Systems
- Code Agent Approach
- Efficiency in Execution
- Expressiveness in Programming
- Security Risks
- Module Access Control
- Isolation Contexts
- Agent Collaboration
- Agent Communication
- Agent Coordination
- Specialized Agent Roles
- Transparent Decision-Making
- Explainable AI
- Response Time Optimization
- Fluid Interactions
- External Knowledge Integration
- Context Management
- Task Decomposition
- Orchestration of Planning
- Dynamic Adjustments
- Python Exception Handling
- Symbolic Structures
- Entity Relationships
- Constraint Modeling
- Knowledge Access Efficiency
- Inference Mechanisms
- Truth Value Evaluation
- Incomplete Information Handling
- Abstract Symbol Mapping
- Real-World Referents
- Expressiveness vs Efficiency
- Computational Resource Constraints
- Local vs Cloud Execution
- Latency Reduction
- Cost Optimization
- Parameter Documentation
- Enumerated Values
- Error Return Formats
- Python Function Definition
- Automated Documentation
- DeepSeek Architecture
- Mixture of Experts
- Reinforcement Learning
- Parameter Efficiency
- Knowledge Base Integration
- Token Prediction
- Next-Token Prediction
- Deep Learning Models
- Transformer Architecture
- Attention Mechanisms
- Context Windows
- Prompt Templates
- Few-Shot Learning
- Zero-Shot Capabilities
- Agent Memory Systems
- Conversation State Management
- External Tool Connection
- API Integration
- Database Connectivity
- System Autonomy Degree
- Code Generation Quality
- Code Testing
- Code Debugging
- Agent Performance Metrics
- Peer Review Process
- Project Planning
- Milestone Reviews
- Feedback Integration
- Interactive Agent Design
- User Experience Considerations
- Agent Interface Design
- Command Parsing
- Intent Recognition
- Natural Language Understanding
- Tool Selection Logic
- Parameter Extraction
- Content Generation
- Content Refinement
- Multimodal Agents
- Education Applications
- Code Assistant Applications
- Knowledge Graph Traversal
- Graph Database Integration
- Relationship Types
- Entity Properties
- Knowledge Graph Construction
- Knowledge Graph Maintenance
- Knowledge Inference
- Reasoning Chain Documentation
- Decision Transparency
- Interpretable Agent Behavior
- Domain-Specific Knowledge
- Tool Interoperability
- Result Validation
- Output Formatting
- Code Documentation Generation
- Error Messages Interpretation
- LLM Limitations
- Context Window Management
- Tool Selection Strategies
- System Architecture Design
- Agent Deployment Strategies