AI Agent & RAG Development Path

Advanced training for building autonomous agents and retrieval-augmented generation systems

Learning Objectives

Upon completing this specialized learning path, you will be able to:

  • Design and implement autonomous AI agents with specific capabilities
  • Create effective retrieval systems that augment LLM knowledge
  • Build multi-agent systems that can collaborate on complex tasks
  • Implement effective memory and context management systems
  • Develop architectures that balance autonomy with appropriate safeguards

Learning Path Structure

Prerequisites

Before starting this advanced path, ensure you have:

  • Solid understanding of LLM fundamentals (see the LLM Handling path)
  • Basic programming skills (Python preferred)
  • Familiarity with API integrations
  • Understanding of vector embeddings concepts

Phase 1: Retrieval-Augmented Generation Fundamentals

Master the core concepts and implementation of RAG systems.

Vector Embeddings

  • Understanding embeddings and vector spaces
  • Selecting and implementing embedding models
  • Optimizing embeddings for specific domains

Vector Databases

  • Setting up and configuring vector databases (Chroma, Pinecone, etc.)
  • Indexing strategies for different content types
  • Optimizing search performance and relevance

Retrieval Mechanisms

  • Query formulation and processing
  • Hybrid search techniques
  • Reranking and relevance scoring

Project: Simple RAG System

Build a basic RAG system that indexes a collection of documents and answers questions based on their content.

Phase 2: Agent Fundamentals

Learn how to build autonomous agents with specific capabilities.

Agent Architecture

  • Core components of an AI agent
  • Planning and reasoning mechanisms
  • Tool use and function calling

Memory Systems

  • Short-term and conversational memory
  • Long-term knowledge storage
  • Memory retrieval and relevance

Tool Integration

  • Designing and implementing agent tools
  • API and external service connections
  • Result processing and incorporation

Project: Task-specific Agent

Build an agent that can perform a specific task autonomously, such as gathering information from the web or analyzing data.

Phase 3: Advanced Agent & RAG Integration

Combine agents and RAG systems into powerful, knowledge-aware autonomous systems.

RAG-Enhanced Agents

  • Integrating retrieval capabilities into agents
  • Dynamic knowledge acquisition
  • Self-updating knowledge bases

Multi-Agent Systems

  • Agent communication protocols
  • Role specialization and task delegation
  • Orchestrating multiple agents with frameworks like CrewAI

Reliability and Safety

  • Implementing guardrails and safety measures
  • Error handling and recovery mechanisms
  • Monitoring and logging agent activities

Capstone Project

Build a multi-agent system with RAG capabilities that can solve complex, open-ended problems by combining information retrieval, reasoning, and specific domain tools.

Recommended Technologies

Agent Frameworks

  • LangChain
  • CrewAI
  • AutoGPT
  • LlamaIndex

Vector Databases

  • Pinecone
  • Chroma
  • Weaviate
  • Qdrant

LLM Providers

  • OpenAI (GPT-4, GPT-3.5)
  • Anthropic (Claude)
  • Mistral AI
  • Google (Gemini)

Development Tools

  • Python
  • Jupyter Notebooks
  • FastAPI/Flask
  • Docker
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