Core AI Development
Foundation
Prompt Engineering
Optimizing AI Communication
The art and science of crafting precise instructions to guide AI models toward desired outputs. It involves strategically designing prompts to maximize accuracy, relevance, and consistency.
🔄 Prompt Engineering Process
Define Goal
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Craft Prompt
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Test & Iterate
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Optimize
✨ Key Techniques:
- Zero-shot and few-shot prompting
- Chain-of-thought reasoning
- Role-based prompting
- Context window optimization
- Temperature and parameter tuning
Complexity:
Enhancement
RAG (Retrieval-Augmented Generation)
Dynamic Knowledge Integration
A technique that enhances AI responses by retrieving relevant information from external knowledge bases in real-time, combining the power of large language models with up-to-date, domain-specific data.
🔄 RAG Process Flow
Query Input
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Search Database
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Retrieve Context
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Generate Response
🎯 Core Components:
- Vector databases and embeddings
- Semantic search algorithms
- Document chunking strategies
- Relevance scoring and ranking
- Context injection and synthesis
Complexity:
Customization
Fine Tuning
Model Specialization
The process of adapting pre-trained AI models to specific tasks or domains by training them on specialized datasets, creating models that excel in particular use cases while maintaining general capabilities.
🔄 Fine Tuning Pipeline
Prepare Data
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Select Base Model
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Train & Validate
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Deploy Model
🛠️ Techniques & Methods:
- Parameter-efficient fine-tuning (PEFT)
- Low-rank adaptation (LoRA)
- Task-specific dataset preparation
- Hyperparameter optimization
- Model evaluation and validation
Complexity:
Advanced AI Systems
Autonomous
Agentic AI
Autonomous AI Systems
AI systems that can operate independently, make decisions, and take actions to achieve specific goals without constant human oversight. These agents can plan, execute, and adapt their strategies dynamically.
🔄 Agentic AI Cycle
Perceive Environment
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Plan Actions
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Execute Tasks
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Learn & Adapt
🎯 Agent Capabilities:
- Goal-oriented behavior
- Multi-step reasoning and planning
- Tool usage and API integration
- Memory and state management
- Adaptive learning mechanisms
Complexity:
Protocol
MCP (Model Context Protocol)
Standardized AI Communication
A standardized protocol that enables AI models to seamlessly connect with external tools, databases, and services, providing a unified interface for context sharing and resource access across different systems.
🔄 MCP Integration Flow
Request Context
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Protocol Handshake
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Data Exchange
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Context Update
🌐 Protocol Features:
- Standardized API endpoints
- Real-time context synchronization
- Multi-modal data support
- Security and authentication
- Cross-platform compatibility
Complexity:
Network
A2A (Agent-to-Agent)
Multi-Agent Collaboration
Advanced systems where multiple AI agents communicate, collaborate, and coordinate with each other to solve complex problems, enabling distributed intelligence and specialized task delegation.
🔄 A2A Coordination
Task Distribution
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Agent Communication
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Result Synthesis
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Collective Output
🤝 Collaboration Models:
- Hierarchical agent structures
- Peer-to-peer communication
- Consensus mechanisms
- Load balancing and scheduling
- Conflict resolution protocols
Complexity: