Agent & RAG Learning Path
Build intelligent agents and implement Retrieval Augmented Generation for more capable AI systems
Last updated: May 2025
Learning Objectives
By the end of this advanced learning path, you will be able to:
- Build AI agents that can perform complex, multi-step tasks
- Implement effective RAG systems for knowledge-intensive applications
- Design and deploy multi-agent systems for collaborative tasks
- Integrate external tools and APIs with agents
- Create memory and knowledge retrieval systems for AI applications
Path Information
Details
- Level: Intermediate to Advanced
- Duration: 6-8 weeks
Prerequisites
- Solid understanding of LLM capabilities and limitations
- Experience with API integration
- Basic knowledge of vector databases
- Programming proficiency in Python or JavaScript
Target Audience
AI developersData engineersSolution architectsML engineers
Learning Path Structure
Phase 1: Agent Fundamentals
Understand the core concepts of AI agents and their implementation.
Agent Architecture
- Core components of an AI agent
- Agent models (reactive, goal-based, utility-based)
- Planning and execution frameworks
Frameworks & Tools
- LangChain vs. CrewAI
- AutoGPT and BabyAGI
- Setting up development environments
Basic Agent Implementation
- Creating a single-task agent
- Tool and API integration
- Testing and debugging agents
Phase 2: Retrieval Augmented Generation
Master the techniques for augmenting LLMs with external knowledge.
Knowledge Representation
- Vector embeddings and semantic search
- Document chunking strategies
- Metadata and filtering
Vector Databases
- Setting up Pinecone, Weaviate, or Chroma
- Indexing and querying vector databases
- Hybrid search techniques
RAG Implementation
- Building a basic RAG pipeline
- Query reformulation techniques
- Evaluating RAG quality
Phase 3: Advanced Agent Systems
Build sophisticated multi-agent systems with memory and advanced capabilities.
Multi-Agent Systems
- Agent roles and specialization
- Inter-agent communication
- Orchestration and coordination
Memory Systems
- Short-term and long-term memory
- Contextual recall mechanisms
- Memory indexing and retrieval
Capstone Project
- Design a complex agent system with RAG capabilities
- Implement evaluation metrics
- Deploy and monitor the system
Learning Process
For best results with this advanced learning path:
- Complete each phase sequentially
- Build increasingly complex projects as you progress
- Focus on a specific domain application (e.g., customer service, research assistant)
- Continuously evaluate and refine your implementations